IDENTIFICATION OF SILICIFICATION USING …...man from the ITC. I thank all lecturers and staff members of ESA (Earth System Analysis) department of ITC, including Prof. Dr. Freek van

IDENTIFICATION OF SILICIFICATION USING

AIRBORNE THERMAL INFRARED DATA IN THE PANORAMA,

PILBARA, AUSTRALIA

Munkhjargal TODBILEG March 2003

IDENTIFICATION OF SILICIFICATION USING AIRBORNE THERMAL INFRARED DATA

IN THE PANORAMA, PILBARA, AUSTRALIA

by

Munkhjargal TODBILEG Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Mineral Resources Exploration and Evaluation specialisation Degree Assessment Board Prof. Dr. Martin Hale (Chairman) ESA Department, ITC Dr. B. G. H. Gorte (External Examiner) TU-Delft Drs. Frank van Ruitenbeek (1st supervisor) ESA Department, ITC Dr. Ben Maathuis (2nd supervisor) WRS Department, ITC Dr. Paul M. van Dijk (Observer) ESA Department, ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the In-ternational Institute for Geo-information science and earth observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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Dedicated to: Itgelmaa Todbileg

Abstract

The aim of this research was to detect silicificied rock units using the airborne thermal infrared (TIR) multispectral data of MASTER instrument in the Panorama Volcanic-associated Massive Sulphide (VMS) district in the Pilbara, Western Australia. A remote identification of silicified rock helps to locate hydrothermal discharge zones and to focus exploration on key areas in favorable zones at Pano-rama VMS district. For the detection of silicified rock units from TIR data five different methods were used and com-pared, including a spectral band ratio method, decorrelation stretching, emissivity normalization, ref-erence channel emissivity, and alpha residuals method. Additionally, the reference point calibration method was used to correct for atmospheric and undesired surface effects. Results of the work were validated using ground data, including the geology and alteration maps, whole-rock geochemistry data and laboratory emissivity spectra. The interpolated silicification map using the inverse distance moving average method represented by the SiO2/Al2O3 ratio from the whole-rock geochemistry can indicate result of convective hydrothermal processes, which were keen to form VMS deposit at the Panorama area. The interpolated silicification map coincides with altera-tion and SiO2 mass transfer maps of Brauhart et al., 1998 and 2001 respectively. Results of the mapping silicification from airborne TIR data show that the ratio of (band 43/ band 45) of atmospherically corrected alpha residuals images correlates highest (correlation coefficient of 0.78) with a SiO2/Al2O3 ratio derived from whole-rock analysis. The SiO2/Al2O3 ratio is used a measure for silicification. It is concluded that the alpha residuals method is the best method of the three methods for emissivity approximation if the data is subsequently corrected for atmospheric influences using reference point calibration. Without correction for atmospheric influences the spectral band ratio of bands (44+45+46)/43 from the TIR radiance data at sensor maps the silicification best. This ratio cor-relates well with the SiO2/Al2O3 ratio (correlation coefficient of 0.72). For the visual enhancement the decorrelation-stretching method maps the silicified rock units best. Also the hue wiping was applied on the decorrelation stretched image to remain only siliceous units.

Acknowledgement

I had been spent pleasant time during the one year and six months from the September of 2001 until the March of 2003 of the study in ITC, Enschede in the Netherlands. I am grateful to both the Government of Mongolia and the Netherlands, through the fellowship pro-grammes, for the financial support to undertake the study. I appreciate who had given me significant recommendations during pursuing the scholarship and applying to the ITC: Prof. Dr. Ochir Gerel, “Prof. Dr. Sh. Batjargal”, and Prof. Dr. A. Bayasgalan from the Mongolian University of Science and Technology, and Drs. Boudewijn de Smeth, Prof. Dr. John van Genderen, and Mrs. Adrie Schegget-man from the ITC. I thank all lecturers and staff members of ESA (Earth System Analysis) department of ITC, including Prof. Dr. Freek van der Meer, Dr. Tsehaie Woldai, Dr. Cees van Westen and others, who taught me firstly to familiarize in field of GIS and Remote Sensing; Dr. Paul Van Dijk and Drs. Boudewijn de Smeth, who nominated me as a MSc (Master of Science) student from the PM (Professional Master) level; Dr. Phil Westerhof, who gave us notable lectures and advices in Mineral Exploration field; and Dr. John Carranza, who made the creative review on the literature research report and gave the impor-tant suggestion related to this MSc research. My thanks go again to the Drs. Boudewijn de Smeth and also to Prof. Dr. Martin Hale, who led the remarkable fieldwork in the famous Iberian Pyrite Belt in Spain and Portugal in June of 2002. Special acknowledgement is given to my supervisors of the thesis, Drs. Frank Van Ruitenbeek and Dr. Ben Maathuis, for their support on my research work as well as their guidance, critical reviews and effective research detail corrections on my thesis drafts are very appreciated. I am thankful also to my first supervisor Drs. Frank Van Ruitenbeek, who managed the overall research progress. I sincerely acknowledge to the CSIRO Exploration & Mining in Perth, Australia, for providing the important data of the Panorama area for the research. I thank my fellow students in the Mineral Exploration and Evaluation specialization: Ariadna Suarez Rojas (Cuba), Ninik Suryantini (Indonesia), Jerry Ahadjie (Ghana), Gift Rukezo (Zimbabwe), Megersa Bekele (Ethiopia), Jorge Gaviria (Columbia) and Paul Wachenje (Kenya), for a good interna-tional friendship during the study and fieldwork. I acknowledge widely to all my country fellows: Sh. Amarjargal, B. Aminchimeg, Dr. V. Battsengel, B. Bat, Ch. Bolorchuluun, R. Gankhuyag, D. Javkhlanbold, B. Oyungerel, B. Oyuntulkhuur, A. Tsol-mon, O. Tsolmongerel, Kh. Tseedulam, D. Ulziisaikhan, and J. Undariya, because we were sharing together all common grounds within us, however we studied in multi-variable programmes of ITC from Mongolia. Finally, my heartfelt gratitude goes to my family, including my parents, brothers and sisters, most es-pecially to my wife S. Khishigtsagaan and my daughter Itgelmaa for their support, care and patience. Munkhjargal TODBILEG Enschede, 2003.

Contents

Chapter 1. Introduction

1.1. Introduction 1 1.2. Problem definition 2

1.2.1. Silicification recognition 3 1.3. Motivation for the research 4 1.4. Research objectives 5

1.4.1. Silicification from ground data 5 1.4.2. Mapping silicification from airborne TIR data 6

1.4.3. Comparison of various methods and validation 6

Chapter 2. Background

2.1. Theoretical background 8 2.1.1. Imaging spectroscopy 8 2.1.2. TIR remote sensing 9 2.2. Emissivity estimation 11 2.2.1. Quartz or silica determination 14

Chapter 3. Resources

3.1. Research area 16 3.1.1. Geology 16 3.1.2. Hydrothermal alteration 18 3.1.3. VMS deposits and silica 20 3.2. Dataset and equipment 21

Chapter 4. Methodology

4.1. Research scheme 22 4.2. Methods 22

4.2.1. Exploratory data analysis 22 4.2.2. Point interpolation methods 23 4.2.3. Decorrelation stretching 23 4.2.4. Spectral band ratio method 24 4.2.5. Emissivity normalization 24 4.2.6. Reference channel emissivity 24 4.2.7. Alpha residuals method 25 4.2.8. Reference point calibration method 26 4.3. Comparison and validation 27

Chapter 5. Silicification from ground data

5.1. Siliceous geological map units 28 5.1.1. Coonterunah Group 28

5.1.2. Warrawoona Group 28 5.1.3. Sulphur Springs Group 29 5.1.4. Gorge Creek Group 30 5.1.5. De Grey Group 31 5.1.6. Carlindi Granitoid Complex 31 5.1.7. Unassigned rocks 32 5.1.8. Fortescue Group 32 5.1.9. Cenozoic Geology 32 5.2. Whole-rock geochemistry 33

5.2.1. Exploratory data analysis 34 5.2.2. Point interpolation and classification 35

5.3. Ground emissivity spectra 37

Chapter 6. Mapping silicification from airborne TIR data

6.1. Exploratory data analysis 41 6.1.1. Comparison of the MASTER TIR data with the laboratory spectra 43 6.2. Image processing and analysis 44 6.2.1. Decorrelation stretching 44 6.2.2. Spectral band ratio 46 6.3. Emissivity approximation methods 47 6.3.1. Emissivity normalization method 47 6.3.2. Reference channel emissivity 52 6.3.3. Alpha residuals method 55

Chapter 7. Comparison and validation

7.1. Introduction 59 7.2. Comparison in visual enhancement 59 7.3. Correlation with whole-rock geochemistry 61 7.4. Spectral comparison 64 7.5. Discussion 66

Chapter 8. Atmospheric correction

8.1. Introduction 67 8.2. Reference point calibration method 67 8.3. Validation after atmospheric correction 75 8.4. Discussion 78

Chapter 9. Conclusion

9.1. Conclusion 79 9.1.1. Silicification from ground data 79 9.1.2. Mapping silicification from airborne TIR data 79 9.2. Recommendation 80

List of figures

1. Figure 1.1. Results of a field measurement of the emissivity of a quartz sand compared to a laboratory measurements of directional hemispherical reflectance of the same sand. c) True emissivity of the quartz sand after removal of reflected downwelling radiance, d) Laboratory spectrum of directional hemispherical reflectance of the quartz sand (from Salisbury and D’Aria, 1992), page 4.

2. Figure 1.2. The general flowchart of the research work; page 5. 3. Figure 2.1. Optical constants of quartz, SiO2, from Spitzer and Klienman, 1960 (Clark, 1999),

page 8. 4. Figure 2.2. Solar irradiance B (λ, 6000 K) reflected from Earth’s surface (broken line) and

Earth-emitted radiance B (λ, 300 K) (solid line), for emissivity=0.9, i.e. reflectivity=0.1 (from Dash et al., 2002), page 11.

5. Figure 2.3. The hemispherical reflectance spectra of tectosilicate minerals with particle size 0-45 um: a) Quartz, b) Orthoclase (feldspar); page 15.

6. Figure 3.1. Location of the research area, Panorama district, Pilbara block, Western Australia; page 16.

7. Figure 3.2. Physiography of Panorama: typical rugged terrain underlying by greenstones, page 16.

8. Figure 3.3. The simplified geology map of the Panorama district and VMS deposits; page 17. 9. Figure 3.4. Alteration map and VMS deposits in the research area, Panorama district, Austra-

lia (Brauhart et al., 1998), page 19. 10. Figure 3.5. Mass transfer map of SiO2 (wt %) (Brauhart et al, 2001), page 20. 11. Figure 3.6. Mass transfer diagram of SiO2 (wt %) for andesite-basalt and granophyric granite,

showing the average, standard deviation (ticks), and range (outer circles) of SiO2 values for each alteration facies (Brauhart et al. 2001), page 20.

12. Figure 4.1. The detail flowchart of the research methodology; page 22. 13. Figure 4.2. Illustration of linear relationship between Ec derived from TIMS data with ra-

diosonde data and Ec derived with deviate atmospheres for two view angles (nadir and 300) (from Li et al. 1999), page 27.

14. Figure 5.1. All types of cherts including, chert-barite, cherty-iron formations and quartz vein in the Panorama area; page 33.

15. Figure 5.2. The simplified geology of the Panorama district and sample points of whole-rock geochemistry; page 33.

16. Figure 5.3. The histogram and normal Q-Q plot of SiO2 content, page 34. 17. Figure 5.4. The histogram and boxplot of the SiO2/Al2O3 ratio, page 34. 18. Figure 5.5. The scatter graph between SiO2/Al2O3 ratio and SiO2 content in the volcanic pile;

page 35. 19. Figure 5.6. a) Silicification in volcanics represented by SiO2/Al2O3 ratio, interpolated by the

inverse distance moving average method and points show VMS deposits, legend represents the degree of silicification; b) Ordinary kriging of SiO2/Al2O3 ratio, page 36.

20. Figure 5.7. Visual comparison of silicification and alteration facies maps; page 36. 21. Figure 5.8. The classified silicification map: weak or least altered, moderate and strong al-

tered; page 37.

22. Figure 5.9 a) Sample points of rock emissivity spectra on the simplified geology; b) Labora-tory emissivity spectra of the sample PS06 on marker chert, which has 3 (I, II, III) measure-ments; page 38.

23. Figure 5.10. Laboratory emissivity spectra of the samples, page 38. 24. Figure 5.11. a) Laboratory emissivity spectra of rocks: PK03BI – rhyolite, PS004I – dacite,

PK009II – andesite-basalt, PS020AII – microdiorite; page 39. 25. Figure 5.12. The reflectance spectra of different igneous rocks in the 8–14 µm wavelength

range: G – granite, S- syenite, B- basalt, D- dunite (from Salisbury and D’Aria, 1992); page 39.

26. Figure 5.13. Laboratory emissivity spectra on the sample point PS009 (dacite), page 40. 27. Figure 6.1. Boxplots of MASTER TIR bands, page 41. 28. Figure 6.2. Atmospheric transmittance, mid-infrared is compared to scaled gray-body spectra,

page 42. 29. Figure 6.3. Histograms and normal probability Q-Q plots of the TIR radiance data at sensor,

page 43. 30. Figure 6.4. Spectra in sample points: (a) MASTER TIR data in rock sample points, and (b) the

laboratory emissivity spectra in the point PK04 (rhyolite), page 44. 31. Figure 6.5. a) The standard false-color composite image (pixel size 15 m) of MASTER TIR

band 47, 44 and 42 for red, green and blue respectively, b) The digital elevation model of the Panorama with the pixel size 10m; page 45.

32. Figure 6.6. a) The decorrelation-stretched image of the MASTER TIR data in the Panorama district; page 46.

33. Figure 6.7. a) The band ratio (48/44) image, b) The color-ratio-composite image of band ra-tios (47/46 – red, 45/44 – green, 44/43 – blue), page 47.

34. Figure 6.8. The minimum, maximum, mean and standard deviation line of emissivity values on the wavelength region 8 – 11 µm, page 48.

35. Figure 6.9. a) The temperature image: warmer area is in lighter tone, b) the emissivity image of the band 44; page 49.

36. Figure 6.10. The color composite of emissivity images, the band 47, 44 and 42 for RGB re-spectively, page 49.

37. Figure 6.11. Visual comparison between (a) the silicification map from SiO2/Al2O3 ratio, the emissivity image of the band 44 (b); page 50.

38. Figure 6.12. Emissivity spectra of some representative points from emissivity images derived using the emissivity normalization, page 50.

39. Figure 6.13. a) The emissivity image of the band 44 in the north part of Panorama, b) Chert layers from geology maps of Van Kranendonk (2000) and Brauhart et al. (1998); page 51.

40. Figure 6.14. The minimum, mean and maximum spectral line of the reference channel emis-sivity; page 52.

41. Figure 6.15. (a) The temperature image, legend in K and (b) the emissivity image of the band 44 derived using the reference channel emissivity method, page 53.

42. Figure 6.16. The color composite image where band 47, 44 and 42 are for RGB respectively; page 53.

43. Figure 6.17. a) The silicification map of geochemistry (SiO2/Al2O3 ratio); b) the emissivity image of the band 44, which was filtered by average 3x3 pixels, page 54.

44. Figure 6.18. The chert layer interpretation (white lines) on the band 44 of the reference chan-nel emissivity image in the north part of Panorama, Sulphur Springs (ss) VMS deposit point for the map reference; page 54.

45. Figure 6.19. Emissivity spectra of some representative points from emissivity images derived using the reference channel emissivity, page 55.

46. Figure 6.20. Alpha values of the alpha residuals method: a) Spectral profiles for minimum and maximum, mean and standard deviation of alpha values, b) The histogram: band 1 to band 7 means band 42 to band 48 respectively, page 56.

47. Figure 6.21. Alpha residual spectra from the MASTER data on some sample points; page 56. 48. Figure 6.22. a) The alpha residual image of band 44, legends of alpha values, b) The color

composite image of bands 47, 44 and 42 for red, green and blue respectively, page 57. 49. Figure 6.23. Visual comparison of alpha residuals image of the band 44 (a), and the silicifica-

tion map (b) of the SiO2/Al2O3 ratio, page 58. 50. Figure 6.24. The chert layers (white lines) on the band 44 of the alpha residuals image in the

north part of Panorama, page 58. 51. Figure 7.1. Color composite images: a) geology map, b) decorrelation stretching, c) spectral

ratio, d) emissivity normalization, e) reference channel emissivity, f) alpha residuals method; page 60.

52. Figure 7.2. The band combination ratio image of (44+45+46)/(43+47) from the reference channel emissivity in the volcanic pile of north part of Panorama, and ground sample points of the SiO2/Al2O3 ratio value of geochemistry; page 61.

53. Figure 7.3. The correlation graph between the band combination ratio of (44+45+46)/(43+47) from the reference channel emissivity images and SiO2/Al2O3 ratio, page 62.

54. Figure 7.4. The scatterplot between the band combination ratio of (44+45+46)/43 from the original TIR radiance data and SiO2/Al2O3 ratio; page 63.

55. Figure 7.5. The band combination ratio image of (44+45+46)/43 from the TIR radiance data, white points are VMS deposits, page 63.

56. Figure 7.6. The spectral comparison with the laboratory data; page 66. 57. Figure 8.1. The reference point locations and their numbers on the geology map of Van Kra-

nendonk (2000); page 68. 58. Figure 8.2. Spectra of emissivity images and laboratory data from some reference points; page

69. 59. Figure 8.3. Atmospheric transmittance expressed by “b” value; page 71. 60. Figure 8.4. Percent differences between means of emissivity images before and after atmos-

pheric correction; page 72. 61. Figure 8.5. Atmospherically corrected (continual line) and uncorrected (dashed line) emissiv-

ity spectra of representative rock samples derived from emissivity images estimated by the emissivity normalization method; page 73.

62. Figure 8.6. Atmospherically corrected (continual line) and uncorrected (dashed line) emissiv-ity spectra of sample points derived from emissivity images estimated by the reference chan-nel emissivity method; page 73.

63. Figure 8.7. The laboratory emissivity spectra at sample points PK03 and PS22, page 74. 64. Figure 8.8. Atmospherically corrected and uncorrected emissivity spectra of sample points de-

rived from images estimated by the alpha residuals method; page 74.

65. Figure 8.9. Comparison of emissivity spectra derived using the emissivity normalization (EN), reference channel emissivity (RCE), alpha residuals and laboratory data (Lab. data) on sample points; page 75.

66. Figure 8.10. The scatterplot between the band ratio of 43/45 of the atmospherically corrected alpha residuals image and SiO2/Al2O3 ratio; page 77.

67. Figure 8.11. The band ratio image of 43/45 derived using the atmospherically corrected alpha residuals images; page 77.

List of Tables

1. Table 3.1. Spectral characteristics of the TIR MASTER channels; 21 2. Table 6.1. Summary statistics for the airborne TIR radiance data at the MASTER sensor; 41 3. Table 6.2. The correlation matrix among TIR bands; 43 4. Table 6.3. General statistics of emissivity values estimated by the emissivity normalization;

48 5. Table 6.4. General statistics of the reference channel emissivity. 52 6. Table 6.5. General statistics of alpha residual images 55 7. Table 7.1. Correlation of the SiO2/Al2O3 ratio versus emissivity bands derived using

emissivity approximation methods in the north part of Panorama; 61 8. Table 7.2. Correlation of the SiO2/Al2O3 ratio versus emissivity band ratios derived using

emissivity images and the MASTER sensor data in the north part of Panorama. 62 9. Table 7.3. Correlation of the SiO2 content versus emissivity bands derived using emissivity

approximation methods in the Panorama; 64 10. Table 8.1. The description of reference points in geology and alteration; 67 11. Table 8.2. Comparison of the average emissivity values of the MASTER emissivity derived

using the emissivity normalization and the µFTIR laboratory spectra at reference points; 69 12. Table 8.3. Comparison of the average emissivity derived from the MASTER TIR data using

the reference channel emissivity method and the µFTIR laboratory spectra at reference points; 70

13. Table 8.4. Comparison of general statistics of emissivity images with (2) and without (1) at-mospheric correction derived using the emissivity normalization method; 71

14. Table 8.5. Comparison of general statistics of the reference channel emissivity images with (2) and without (1) atmospheric correction; 71

15. Table 8.6. Correlation of the SiO2/Al2O3 ratio versus atmospherically corrected emissivity bands derived using emissivity approximation methods in the north part of Panorama; 76

16. Table 8.7. Correlation of the SiO2/Al2O3 ratio versus atmospherically uncorrected emissivity bands derived using emissivity approximation methods; 76

17. Table 8.8. Correlation of the SiO2/Al2O3 ratio versus emissivity band ratios derived using at-mospherically corrected emissivity images. 76

IDENTIFICATION OF SILICIFICATION USING AIRBORNE THERMAL INFRARED DATA IN THE PANORAMA, PILBARA, AUSTRALIA

1

Chapter 1. Introduction

“In remote sensing studies, … identification requires both discrimination and a spectral band shape that can be considered

unambiguous and that can be converted to an appropriate unit for comparison to laboratory measurements.”

Laurel Kirkland et al., 2001.

1.1. Introduction

The thesis for the identification of silicification using the airborne TIR data in the Panorama consists of nine chapters including the Introduction, Background, Resources, Methodology, Silicification from ground data, Mapping silicification from airborne data, Comparison and validation of various meth-ods, Atmospheric correction and Conclusion. In the introduction chapter, the research problem was determined, relates to the identification and mapping of silicification from airborne TIR data. The silicification is one main type of hydrothermal alterations, which change a composition of country rocks. Therefore it is the problem to identify sili-cified rocks from originally silicic ones. And the motivation and methodology for the research work were shortly described. In the background chapter, the theoretical background for imaging spectroscopy and TIR remote sens-ing, and the literature review for emissivity estimation and silica determination were explained as suit as possible way. In the resources chapter, the research area and its’ general geology and alteration, and the spatial and compositional relationships between VMS deposits and silicification were briefly included. And also the dataset and equipments were mentioned and used to achieve the aim of this research. The methodology chapter describes all methods of the point interpolation, image processing and analysis, emissivity estimation, and atmospheric correction, which were used in whole part of this work. The chapter 5 is for the silicification from ground data, including siliceous geological map units, whole-rock geochemistry, which concentrates on the SiO2 content and SiO2/Al2O3 ratio, following the point interpolation for the silicification map, and the ground emissivity spectra. The mapping silicification from airborne MASTER TIR data (chapter 6) is the main chapter of this thesis. First of all in this chapter, the original data of MASTER TIR channels was examined by ex-ploratory data analyses. And then image processing and analysis were applied on. Emissivity ap-proximation approaches were presented in two parts, including the emissivity and temperature separa-tion, silicification mapping. Within these processes, various types of comparison and representation were used. And then in the chapter 7, results of various methods were compared each other, and validated using the ground data. The validation work of this stage was applied on atmospherically uncorrected emis-sivity images derived using emissivity approximation methods.


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In the chapter 8, the atmospheric correction using the reference point calibration method was done on emissivity images, also compared with the uncorrected ones and laboratory data, and then validated and discussed. Finally in the chapter 9, the entire work is concluded in one complete sense and some recommenda-tions are added.

1.2. Problem definition

Alteration systems associated to mineral deposits often display mineralogical and geochemical zones (or alteration facies), which extent over several kilometers and can be observed in remotely sensed images. Minerals of alteration are mostly silicate minerals typically quartz, sericite, chlorite, and K-feldspar etc. Within these alteration zones, silicification is an important key component for explora-tion of certain mineral deposits such as VMS (Volcanic-associated Massive Sulphide) and porphyry copper deposits. The spatial and temporal relationships suggest that wall rock alteration is due to reactions caused by the mineralising fluid permeating parts of the wall rocks. Studies of alteration are important because they (a) contribute to our knowledge of the nature and evolution of ore-forming solutions, (b) are of-ten valuable in mineral exploration. The controls of wall rock alteration fall into two groups governed respectively by the nature of host rocks and the nature of ore-forming solutions. Besides the chemistry of host rocks, other factors of importance are their grain size, physical state (e.g. sheared or un-sheared). For example the alteration of K-feldspar to muscovite (sericite):

3KAlSi3O8+2H+ ⇔ KAl3Si3O10(OH)2+2K+ + 6SiO2 K-feldspar muscovite quartz

Hydrogen ion is consumed (hydrolysis) and potassium released thus altering the activity ratio. This will affect the pH of the ore fluid and the degree of dissociation of dissolved HCl, which, in turn, af-fects the degree of combination of metal-chlorine complexes and therefore the solubility of metals in the solution. This is one of many illustrations of the interdependency of alteration and mineralisation. Note also that silica is released by this reaction. This may be precipitated as secondary quartz, so that some silicification accompanies the sericitization, or it may be carried away in solution perhaps to form a quartz vein or veinlet in the vicinity. By similar reaction albite can be hydrolyzed to paragonite and in this case sodium and silica are removed from the rock (Evans, 1993). It is clear from the above considerations and many wall rock alteration studies that extensive me-tasomatism often accompanies the alteration. In some cases the nature of the original rock may be un-certain, but examination of the field relations, petrology and geochemistry may allow the determina-tion of a ‘least altered equivalent’. The relative chemical gains and losses then can be calculated after making assumptions of constant volume or constant alumni content during alteration. This is Gresens’ approach, which was further developed by Grant (1986). During wall rock alteration, although most rock-forming minerals are susceptible to attack by acid solution, carbonates, zeolites, feldspathoids and calcic plagioclase are least resistant; pyroxenes, amphiboles and biotite are moderately resistant, and sodic plagioclase, potash feldspar and muscovite are strongly resistant. Quartz is often entirely unaffected (Evans, 1993). Traditionally all geologists can recognize easily quartz or silica in the field, but nowadays we had moved to study how we can recognize these from remote sensing.


3

The silicon-oxygen bonds of silicate minerals do not cause spectral features in the visible to short-wave infrared region of the spectrum (0.4-2.5 µm) (Hunt, 1977). However, previous spectroscopic studies of inorganic materials (Hunt, 1950 etc) have demonstrated that the stretching vibrations of the Si-O bonds produce very strong fundamental bands in the thermal infrared (TIR) atmospheric window region (8-12 µm), the so-called “reststrahlen bands”. Thus, it is expected that TIR multiband sensors should be able to detect and identify silicified rock units as well. In this case, it is considered that the problem of silicification recognition.

1.2.1. Silicification recognition

Types of wall rock alteration have been described by Meyer & Hemley (1967), by Rose & Burt (1979) and by Evans (1993). Quartz will usually be present in many types of alterations including: Advanced argillic alteration is characterized by dickite, kaolinite (both Al2Si2O5(OH)4), pyrophyllite (Al2Si4O10(OH)2) and quartz. This alteration involves extreme leaching of bases (alkalis and calcium) from all aluminous phases such as feldspars and micas, but is present only if aluminium is not appre-ciable mobilized. When aluminium is also removed it grades into silicification and, with increasing sericite, it grades outwards into sericitization. Sericitization is one of the commonest types of alteration in aluminium-rich rocks such as slates, gran-ites, etc. The dominant minerals are sericite and quartz, pyrite often accompanying them. During the sericitization of granite, the feldspars and micas may be transformed to sericite, with secondary quartz as a reaction by-product, but the primary quartz may be largely unaffected except for the development of secondary fluid inclusions. Chloritization may be present chlorite alone or with quartz or tourmaline in very simple assemblages. Silicification involves an increase in the proportion of quartz or cryptocrystalline silica (i.e. cherty or opaline silica) in the altered rock. The silica may be introduced from the hydrothermal solutions, as in the case of chertified limestone associated with lead-zinc-fluorite-barite deposits, or it may be the by-product of the alteration of feldspars and other minerals during the leaching of bases. Silicification is often a good guide to ore, e.g. the Black Hills, Dakota (Evans, 1993). In the Panorama VMS deposits are associated with seafloor alteration zones in and below silica-carbonate laminitis, at the top of a sequence of tholeiitic intermediate and felsic rocks. In VMS deposits wall rock alteration is usually confined to the footwall rocks. Chloritization and sericitization are the two commonest forms. Metamorphosed VMS deposits commonly show altera-tion effects in the hanging wall (Solomon et al., 1987). Generally, the research problem is related to actual detection and identification of silicification from airborne TIR data. It is also useful to consider difficulty in distinguishing between hydrothermally silicified rocks from unaltered silicic igneous rocks. Emissivity spectra of geologic materials can be quite complex; therefore emissivity studies require as many spectral bands in 8-14 µm TIR window as possible. The ability of any spectral instrument to detect and uniquely identify surface minerals is proportional to the strength, width, and number of bands exhibited by the mineral over the spectral range measured; confidence in the instrument calibra-tion, atmospheric compensation, and conversion to a unit for comparison to laboratory spectra; and the information content of each spectrum. Information content increases with higher spectral resolu-tion, signal-to-noise ratio (SNR), spectral range, and denser sampling interval (Kirkland et al. 2001).


4

1.3. Motivation for the research

A wide range of methods exists to recover emissivity (ε), temperature (T), or both from TIR data over all terrestrial surfaces. Review of previous and current researches helps to select the most appropriate and available methods. In a geological mapping the important consideration is attended to recover spectral shapes of different rock units. Therefore it is best to choose a method, which recover spectral shapes. A strong reststrahlen band occurs only in quartz among silicate minerals, because only quartz displays significant reststrahlen bands at 8 – 14 µm wavelength (Fig. 1.1). The aggregate Si-O stretching vibra-tion bands (reststrahlen bands) of the component minerals of igneous rocks result in broad reflectance peaks or emittance troughs that migrate to longer wavelength for increasingly mafic rock types (Salis-bury and D’Aria, 1992).

Figure 1.1. Results of a field measurement of the emissivity of a quartz sand compared to a laboratory measurements of directional hemispherical reflectance of the same sand. c) True emissivity of the quartz sand after removal of reflected downwelling radiance, d) Laboratory spectrum of directional hemispherical reflectance of the quartz sand (from Salisbury and D’Aria, 1992). An atmospheric correction requires an atmospheric model and local atmospheric information. In this MSc research no atmospheric information and suitable model was available, therefore the only possi-bility is to choose the method of reference point calibration for atmospheric correction. Reference points are selected from ground emissivity spectral data. Generally, alteration zones related to mineral deposits, are best determined with geochemistry, and provide large exploration targets. Therefore results of geochemical analysis can be used for distin-guish between hydrothermally silicified rocks from unaltered silicic igneous rocks. For example from Vearncombe (1996) in the Kangaroo Caves VMS deposit site, during alteration process massive addi-tions of SiO2 are recorded sporadically, with SiO2 content reaching 83% in the dacites and 66% in the andesites. SiO2 in the least altered andesite is 53.7%. Thus the whole-rock geochemistry data for SiO2 content or SiO2/Al2O3 ratio can be used to validate the mapping of silicification from airborne TIR data. The geochemical or mass transfer map of SiO2 is a useful adjunct to alteration mapping in identifying regional vectors to ore at Panorama. Therefore a remote identification of silicification zone or silici-


5

fied horizons helps to locate hydrothermal discharge zones and then to focus exploration on key areas along a favorable horizon at Panorama.

1.4. Research objectives

The aim of this research is to detect and identify silicification zones using the airborne TIR multispec-tral data of MASTER instrument in the Panorama area, Pilbara, Western Australia. Research objectives are:

1. Mapping of silicification from ground data; 2. Mapping of silicification from airborne TIR data; 3. Comparison and validation;

The general flowchart for this study is shown in figure 1.2.

Ground data MASTER TIR data

ComparisonMap silicification

Geostatistics & GIS

Exploratorydata analysis

Approximateemissivity

Thematic mappingEvaluation

Figure 1.2. The general flowchart of the research work;

1.4.1. Silicification from ground data

This objective is supplied by three main data sources, which are geological maps, XRF whole-rock geochemistry analysis and laboratory emissivity spectra. A SiO2 content and SiO2/Al2O3 ratio from XRF whole-rock chemical analysis are used to map silicifi-cation quantitatively in igneous rocks and alteration zones. Geostatistics including an exploratory data analysis and interpolation methods are applied for this task. The reason of choosing the SiO2/Al2O3 ratio for silicification is that mass-balance techniques quantify mass transfer (i.e., how much of an element has been added to, or removed from, an altered rock) by comparing the composition of an altered sample to its least altered equivalent, using element ratio in which the denominator is immo-bile. Elements that are typically immobile in VMS alteration systems (Brauhart et al. 2001) include Al, Ti, Y, Zr, Nb, REE and Th. Chert, quartz veins, quartzites and jasper whatever products of hydrothermal silicification processes and Cenozoic sand deposits are derived from geological and alteration maps of previous studies. Vari-ous types of GIS tools will be applied in this part.


6

A laboratory emissivity spectrum is also used for the mapping of silicification using additional infor-mation from a spectral library.

1.4.2. Mapping silicification from airborne TIR data

An exploratory data analysis is used to describe variations in TIR radiance data. In general, most of the variation in radiant spectral flux measured by TIR is due to differences in sur-face temperature; very little variation results from differences in emissivity of the surface. A useful method of enhancing variations due to emissivity differences and suppressing effects of surface tem-perature is the decorrelation stretch (Gillespie, 1992; Sabine et al. 1994). Therefore the decorrelation contrast stretching technique is applied for processing or enhancement of multispectral TIR images. Relative emissivity is approximated using 4 methods (Reference Channel Emissivity, Normalized Emissivity, Alpha Residuals and Spectral ratio method), which are available in Envi 3.5 and ILWIS 3.11 software. Those methods are applied to confirm and compare results of each other and to validate using ground data. Hook et al. (1992) evaluated three techniques, which are the assumed Channel 6 emittance model (ref-erence channel emissivity), thermal log residuals, and alpha residuals applied on TIMS (Thermal Infrared Multispectral Scanner) data. Of the techniques evaluated, the assumed Channel 6 emittance model is the simplest conceptually. Results indicate that the two techniques (thermal log residuals, and alpha residuals) provide two distinct advantages over the assumed Channel 6 emittance model. First, they permit emissivity information to be derived from all six TIMS channels. The assumed Channel 6 emittance model permits emissivity values to be derived from five of the six TIMS chan-nels. Second, both techniques are less susceptible to noise than the assumed Channel 6 emittance model. Li et al. (1999) evaluated six methods for extracting relative emissivity spectra from TIR images in-cluding: temperature-independent spectral indices (TISI), reference channel emissivity, normalized emissivity, emissivity renormalization, spectral ratio, and alpha emissivity method. All these methods are very sensitive to the uncertainties of atmosphere. They concluded that considering the overall er-ror, the TISI and normalization methods are slightly superior to other methods. Since the concept of normalization method is straight and simple, they select it to deal with real TIMS data. Thus they rec-ommend users to use those two methods for their proper applications. The spectral ratio method, proposed by Watson (1992), is based on the concept that although the spec-tral radiance is very sensitive to small change in temperature the ratio is not. The approach is to com-pute the spectral ratios of adjacent channels. This method provides greater precision in the emissivity ratio than in emissivity itself, and the ratio is roughly similar to the derivative of the emissivity spec-trum. The reference point calibration method is applied for correction of atmospheric effects on airborne MASTER TIR imagery. Li et al. (1999) proposed this method to correct for effects of error in atmos-pheric corrections. The method is based on the linear correlation between relative emissivities (Ec) derived from TIMS data using radiosonde data and relative emissivities derived with the modified atmospheric profiles and also the assumption that there is no spatial variation in the atmospheric con-ditions over the study region in the image.

1.4.3. Comparison of various methods and validation

A comparison of various methods is used to confirm results of all methods and to evaluate them. The comparison using the emissivity spectra measured in a laboratory, in spectral shapes and contrasts is


7

applied first to assess the possibility of MASTER TIR data for mapping of silicification without an atmospheric correction and then with the atmospheric correction using the reference point calibration method. The silicification expressed in emissivity images is validated using the whole-rock geochemistry data. A comparison in visual enhancement is also applied in images created from different methods. Overlying VMS deposit points on the silicification map assesses the spatial relationship between the silicification process and VMS deposits.


8

Chapter 2. Background

2.1. Theoretical background

2.1.1. Imaging spectroscopy

Clark (1999) described that imaging spectroscopy is a new technique for obtaining a spectrum in each position of a large array of spatial positions so that any one spectral wavelength can be used to make a recognizable image. By analysing the spectral features, and thus specific chemical bonds in materials, one can map where those bonds occur, and thus map materials. Such mapping is best done, in this au-thor’s (Clark, 1999) opinion, by spectral feature analysis. All materials have a complex index of refraction:

m = n - jK (1a)

where m is the complex index of refraction, n is the real part of the index, j = (-1)1/2, and K is the imaginary part of the index of refraction, sometimes called extinction coefficient. Example index of refraction (n), and extinction coefficient (K) are shown in Figure 2.1 for quartz.

Figure 2.1. Optical constants of quartz, SiO2, from Spitzer and Klienman, 1960 (Clark, 1999). At fundamental absorption bands, both n and K vary strongly with wavelength, as seen in Figure 2.1. The absorption coefficient (k) is related to the complex index of refraction by the equation:


9

k = 4K/λ, (1b)

where λ is the wavelength of light. The reflection of light, R, normally incident onto a plane surface is described by the Fresnel equation:

R = [(n - 1)2 + K2 ] / [(n + 1)2 + K2 ]. (1c)

The complex index of refraction in Figure 2.1 shows important properties of materials. As one moves to longer wavelengths (left to right in Figure 2.1), the index of refraction decreases to a minimum just before a sharp rise (e.g. at 8.5 and 12.6 µm in Figure 2.1). The minimum is often near or even below n = 1. The wavelength where n = 1 is called the Christensen frequency and usually results in a minimum in reflected light because of the small (to zero) difference in the index of refraction compared to the surrounding medium (e.g. air or vacuum). The location of the observed reflectance minimum is also controlled by the extinction coefficient according to equation 1c. Note that the Christensen frequency sometimes occurs at a wavelength shorter than the maximum in the extinction coefficient (e.g. Figure 2.1). This maximum is called the reststrahlen band: the location of fundamental vibrational stretching modes in the near and mid-infrared. The mid-infrared covers thermally emitted energy, which for the Earth starts at about 2.5 to 3µm, peaking near 10 µm, decreasing beyond the peak, with a shape con-trolled by gray-body emission. The combination of n and K at these wavelengths often results in high reflectance. Emittance. At mid-infrared wavelengths, materials normally receive thermally emitted photons. In the laboratory, one can shine enough light on a sample to ignore emitted photons and measure reflectance, but that can’t be done in typical remote sensing situations. Measuring emitted energy in the laboratory is not easy because all materials emit energy unless cooled to very low temperatures. Trying to meas-ure thermal emission at room temperatures would be like trying to take a picture with a camera with transparent walls and light bulbs turned on inside the camera. However, Kirchoff’s Law states:

ε = 1 - r (1d)

where ε is emissivity and r is reflectance. Several studies have been conducted to show that the rule generally holds. While some discrepancies have been found, they may be due to the difficulty of measuring emittance or due to temperature gradients in the samples. Considering that and the fact that one rarely measures all the light reflected or emitted (usually a directional measurement is made), the law is basically true except in the most rigorous studies where absolute levels and band strengths are critical to the science. In practical terms, small changes in grain size result in spectral changes that are usually larger than the discrepancies in the law (e.g. Clark, 1999 and references therein).

2.1.2. TIR remote sensing

The radiation emitted from a surface in the TIR wavelengths is a function of atmospheric and surface parameters, e.g. surface emissivity and temperature. The emissivity relates to the composition of the surface and is often used for surface constituent mapping. Therefore, it is necessary to perform at-mospheric corrections and to separate the contributions from temperature and emissivity. Unfortu-nately, these two problems are linked together. The direct detection of spectral emissivity from single-time TIR radiance measurements is basically indeterminist. Consider the simple relation (without atmospheric terms):

Rλ=ελBλ(T), (2a)


10

where Rλ is the measured spectral radiance at wavelength λ, ελ is the spectral emissivity at wavelength λ, and Bλ(T) is the blackbody radiance, given by the Planck function:

Bλ(T)=C1λ-5π[exp (C2/λT)-1]-1, (2b) where C1 and C2 are the radiation constants and T is the temperature. Remote sensing of land surface temperature (LST) is based on Planck’s function, which relates the radiative energy emitted by a black body (emissivity =1) to its temperature. However, most natural objects are non-black bodies (0<ε(λ)<1), where the spectral emissivity ε(λ) is the ratio between the radiance emitted by an object at wavelength λ and that emitted by a black body at the same tempera-ture. For non-black bodies, Planck’s function is multiplied by ε(λ) (equation 2a). Kirchoff’s law of radiation states that a body is as good an absorber as an emitter:

( ) ( )λελα ≈ (3)

where ( )λα is the absorptivity. This law holds well for systems at local thermodynamic equilibrium,

i.e. the system can characterize by a single thermodynamic temperature. It can be assumed that the Earth’s surface and the atmosphere up to about 50-70 km are under local thermodynamic equilibrium. If atmospheric effects (upwelling radiance and reflected downwelling irradiance) are separated, and emissivity is known, the temperature of a Lambertian reflector can be determined by reversing equa-tion (2b) (Dash et al. 2002 and references therein):

( )��

��

� +=

1ln 51

2

RC

CT

πλλελ

(4)

Temperature varies with the irradiance history and meteorological conditions. Emissivity is an intrin-sic property of the surface and is independent of irradiance (Gillespie et al., 1999). According to Planck’s law, for every given temperature the maximum amount of radiation is emitted at a particular wavelength λm. Wien’s displacement law states that the product of these temperatures (T) and the corresponding λm is constant:

λm´T=2897.9 µmK (5) For Earth with an ambient temperature of 300K, λm lies at 9.7 µm. Figure 2.2 shows Planck’s function for temperatures of 300K (Earth-emitted radiance) and 6000K (solar irradiance reflected from the Earth’s surface). At around 3.8 µm, for typical emissivity ≈ 0.9 (for vegetation), i.e. reflectivity ≈ 0.1 (Salisbury and D’Aria 1992), the reflected solar irradiance and the Earth-emitted radiance are ap-proximately equal (Dash et al. 2002).


11

The 8–13 µm range also contains the important ‘reststrahlen’ bands, where resonance vibrations asso-ciated with silicon-oxygen bonds in silica tetrahedra cause a decrease in emissivity. As the silica con-tent increases, the emissivity decreases and shifts as wavelengths become longer (Hunt 1980). For radiance measurements, emissivity is defined as:

ελ= Rλ /Bλ(T), (6)

As emissivity is a function of wavelength, it is often referred to as spectral emissivity. In equation (6) the directional dependence and atmospheric terms are ignored as usual for simplicity.

2.2. Emissivity estimation

A wide range of methods designed to recover emissivity (ε), temperature (T), or both from TIR data over all terrestrial surfaces. The estimation of LST and LSE (Land surface emissivity) from passive sensor data is an important and ongoing field of research. Owing to its complex and underdetermined nature, the problem is not fully solved with the accuracy and generality desired by many researchers. In the near future, global LST maps with an accuracy of ±1oC and LSE maps with an accuracy of ±0.005 will be available for many surface types, e.g. from Terra measurements (Dash et al. 2002). The single ‘best’ method for LST/LSE determination does not exist. This review helps to select the most appropriate and available method for a given application and the available information. Despite the increased possibilities offered by multispectral and hyperspectral data, the ‘classical’ methods are still relevant for long term datasets and form a base for the development of new algo-rithms. The methods reviewed in this section are: 1) Alpha-derived emissivity (ADE) method; 6) Mean-MMD method (MMD) 2) Classification method 7) Normalized emissivity method (NEM) 3) Emissivity bounds method 8) Ratio algorithm 4) Graybody emissivity method 9) TES algorithm 5) Reference channel emissivity

Figure 2.2. Solar irradiance B (λ, 6000 K) reflected from Earth’s surface (broken line) and Earth-emitted radiance B(λ, 300 K) (solid line), for emissivity=0.9, i.e. reflectiv-ity=0.1 (from Dash et al., 2002).


12

1) Alpha-derived emissivity (ADE) method. The ADE method (Hook et al., 1992) is based on the “al-pha-residual” technique that preserves shape of the ε spectrum, but not its amplitude, nor T. The “al-pha-residual” spectrum, described below, ranges about a mean of zero. The key innovation of the ADE approach is to utilize an empirical relationship between the average ε and a measure of the spec-tral contrast or complexity in order to restore the amplitude to the alpha-residual spectrum. The regression is based on the common-sense observation that, for a blackbody, the mean emissivity is unity and the spectral variance is zero. For the silicate minerals that comprise much of the geologic substrate of terrestrial scenes deviation from blackbody emissivity occurs because of spectral features (reststrahlen bands) that are localized by wavelength, in TIR window (8-12 µm). For such spectra, the variance is greater than zero and, of course, the mean is less than unity. The regression was established from laboratory reflectance spectra of different rocks, soils, and vege-tation (Salisbury and D’Aria, 1992). The Kirchhoff’s Law (ε=1-r) estimates an emissivity. The alpha residuals are calculated utilizing Wien’s approximation of Planck’s Law. Wien’s approxi-mation introduces errors in simulated radiances (and also in εi) of up to 1% at 300K and 10 µm wave-length. Thus, the alpha residuals are not an unbiased estimation of emissivity, but contain tempera-ture-dependent curvature. The impact on recovered temperatures is minimized because they are first calculated for each band, and then averaged. However, the biased character of the recovered spectra is fundamental to the approach (Gillespie et al. 1999). The strength of the ADE approach is to be more accurate than the model emissivity and NEM tech-niques, largely because it could handle both vegetation and rock surfaces. The weaknesses of the ADE are:

1) The use of Wien’s approximation introduces a bias in the residual spectra that is passed on to the estimated emissivity spectra, but this deficiency was corrected in the hybrid TES algo-rithm;

2) The mathematical core of the ADE technique is more complex than other approaches. 2) Classification. The MODIS instrument team has proposed to use image classification and a spectral library to identify emissivity for land surface (Wan, 1994). Image classification is probably sufficient to discriminate areas of water, snow or closed-canopy vegetation for which, indeed, emissivities can be assigned reliably. Classification-based algorithms are less reliable for geologic substrates, which vary spatially, and which may have radically different emissivity spectra. A fundamental goal is to recover emissivity spectra of geologic surfaces, and these could not be pre-dicted accurately by classification. 3) Emissivity bounds method. S. Jaggi et al. (1992) observed that the independence of T with wave-length permitted unambiguous bounds on T and ε values to be established. For every pixel and every band (considered in isolation) there exist a locus of (T, εI) vectors that are possible solutions. There is family of loci, one for each band, for each pixel. Because T must be the same for all image bands, some (T, εI) pairs can be ruled out as candidate solutions. In effect, possible solutions all fall within a narrow range of T. Emissivity limits for each band are specified by the intersection of the locus of that band and the zone of possible T. The elegant feature of the emissivity-bounds method is that, in principle, no assumptions need to be made. In practice, however, the performance of the algorithm depends on how well emissivity limits are known a priori. The rejected features of this method are:

1) The restriction of the emissivity ranges to useful levels really requires closer a priori estima-tion than may be available in general.


13

2) Establishing useful a priori emissivity limits probably requires some of image classification, with its attendant introduction of artifacts in the products.

The technique does not identify most probable emissivities or temperatures only possible ranges. 4) Graybody emissivity method. It is sometimes possible to examine the scene element at different wavelength λi and λj, chosen such that εi=εj provided this criterion is met, the situation is at least lo-cally deterministic. It is necessary to find T and only a single ε for the 2 bands:

( )( )( )( ) ;

1/exp

1/exp5

2

2

��

�

�

�=��

�

�

�

−−

j

i

j

i

i

j

RR

Tc

Tc

λλ

λλ

( )TBR

i

i=ε (7)

T is readily found by successive approximation. Thereafter, ε may be found for every channel. If more than 2 channels satisfy the requirements of the method, the best solution can be found by least-squares minimization of error. The strength of the technique lies in its ability to recover emissivities for graybodies, regardless of the value of ε. This is not true for algorithms that assume emissivity values (e.g., εi, εmin or εmax). The main weakness is that the basic requirement, εi=εj, is not met for much of the land surface of the earth. Bar-ducci and Pippi (1996) have proposed this technique for use on future scanners that have more TIR channels than are available on ASTER. The technique is inherently unstable and sensitive errors in the fundamental assumption. The effect on recovered T of violating the fundamental assumption, εi=εj, can be estimated by analysing radiance data calculated from a spectral library. For near-graybody spectra the accuracy for T is comparable to NEM; for most rock spectra the graybody assumption is violated and some T errors are in excess of 5 K (for quartzite, errors were as large as 17 K). 5) Reference channel emissivity. The model emissivity method, or reference-channel method (Gilles-pie et al. 1999), assumes that the value of the emissivity for one of the image channels is constant and known a priori, reducing the number of unknowns to the number of measurements. Kahle et al., (1980) developed this method under the name of Channel 6 emittance model in TIMS data. The method assumes the emittance at the ground of every point is equal to a constant in the wavelength region covered by a TIMS channel, typically Channel 6. Since the emissivity for a given channel is known we can isolate T by equation (4). This temperature is used to extract emissivity val-ues for Channels 1-5. The model emissivity approach is robust and has the virtue of simplicity. It produces moderately reli-able results for a wide range of surface materials. Nevertheless, the weaknesses are:

1) It is not capable of producing accurate results for both vegetation and rocks; 2) It will recover inaccurate T and ε for a significant fraction of geologic substrates;

6) Mean – MMD Method (MMD). The MMD algorithm (Matsunaga, 1994) is adapted from the ADE algorithm, but is simpler. Whereas ADE utilizes the empirical relationship between the mean emissiv-ity (ε ) and the variance of alpha-residual emissivities, MMD utilizes the relationship between ε and the total range of the emissivities themselves, the maximum-minimum difference or MMD. The accu-racies of the ADE and MMD algorithms are similar because they are based on the same empirical knowledge. The accuracy of the MMD algorithm depends on the accuracy of the empirical relation-ship between the ε , and MMD and on NE∆T. In general, the MMD algorithm is more accurate but also more sensitive to measurement error than the NEM algorithm. 7) Normalized emissivity method (NEM), a more sophisticated and flexible version (Gillespie et al. 1999) of the model emissivity algorithm, the NEM algorithm permits the wavelength at which εmax


14

occurs to vary from pixel to pixel. The assumed value of εmax is fixed, however, regardless of which image channel it is assigned to. Because the algorithm permits the wavelength of εmax to vary, it is less error-prone than the simpler model emissivity method. NEM shares the virtues of simplicity and over-all reliability with the model emissivity algorithm. Nevertheless, it fails to accommodate the differ-ence in εmax between vegetation and geologic materials. Adjustment of εmax on the basis of image clas-sification would produce the artifacts. 8) Ratio algorithm. One of the most commonly used transformations applied to remotely sensed im-ages. Watson (1992) observed that ratios of bands i and j provided a normalized emissivity spectrum that had the property of preserving spectral shape well, provided that the temperature could be esti-mated even roughly. On the other hand, the technique had no way of recovering the actual ε, or T. 9) TES (Temperature/emissivity separation) algorithm. The TES algorithm (Gillespie et al. 1999) is developed for use on ASTER’s TIR data. TES algorithm hybridizes two established algorithms, first estimating the temperature and band emissivities by the NEM, and then normalizing the emissivities by their average value. Next, an empirical relationship adapted from the Alpha Residual method is used to predict the minimum emissivity from the spectral contrast (MMD) of the normalized values, permitting recovery of the emissivity spectrum with improved accuracy. TES uses an iterative ap-proach to remove reflected sky irradiance. The significant advance of the TES algorithm is to produce unbiased and precise estimate of emissivi-ties and, therefore, improved estimates of surface temperatures for the land surface. For most scenes the TES algorithm can recover emissivities with an accuracy and precision of 0.010-0.015. Major limitations on TES arise from 2 main sources: (1) the reliability of the empirical relationship between emissivity and spectral contrast, and (2) compensation for atmospheric factors.

2.2.1. Quartz or silica determination

The behaviour of silicate mineral spectra in the thermal infrared have been known for some time (e.g., Launer, 1952; Lyon, 1962; Salisbury et al., 1987), and a firm theoretical basis exist for interpreting silicate mineral spectra in terms of their crystal chemistry (e.g. Lazarev, 1972; Farmer, 1974; Karr, 1975). In one of the previous studies, Vincent and Thompson (1972) and Vincent, Thompson, and Watson (1972) used an imaging two-band radiometer to show that quartz-rich regions can be differentiated from regions that lack silicates, based on ratios of an 8.2- to 10.9-µm band to a 9.4- to 12.1-µm band. Their work also demonstrated both the utility of an imaging instrument and the advantages of the strong silicate band for remote sensing. Kahle and Rowan (1980) investigated this approach in more detail using a six-band, 8.3- to 13-µm range radiometer. They used statistical techniques to define type regions, and then identified materials using ground truth. Spectra of igneous rocks are a composite of the spectra of their component minerals and are conse-quently more complicated than individual mineral spectra (e.g. Lyon, 1965; Vincent and Thompson, 1972; Hunt and Salisbury, 1974; Salisbury et al., 1988; Thompson and Salisbury, 1993). Studies (Sabine et al. 1994) with airborne TIR data of TIMS have utilized variations in these funda-mental bands for quantitative estimation of granitoid composition. Sabine et al. (1994) have produced images that quantitatively depict modal and chemical parameters of granitoid using an image-processing algorithm called MINMAP that fits Gaussian curves to normalized emittance spectra re-covered from TIMS radiance data.


15

Ninomiya (1995) showed that TIR spectra of igneous rocks measured in a laboratory correlate well with their SiO2 content, and he has developed the neural network approach to estimate SiO2 content directly from the spectra convolved into the ASTER band combinations with an acceptable accuracy. The hemispherical reflectance spectra of silicate minerals including quartz and feldspars are shown from the ASTER spectral library (fig. 2.3).

a)

b)

Figure 2.3. The hemispherical reflectance spectra of tectosilicate minerals with particle size 0-45 um: a) Quartz, b) Orthoclase (feld-spar);


16

Chapter 3. Resources

3.1. Research area

The research area is the Panorama district, Pilbara block, Western Australia (Fig. 3.1). The geo-graphical coordinate at the center of the area is approximately 119o10’E and 21o10’S (fig. 3.3). The area (327 km2) for this study is chosen by the extent of MASTER TIR imagery scene and called the Panorama area. The region is semiarid. The area has a bimodal topography that reflects the bedrock geology; green-stones outcrop as strike-controlled ridges with a maximum height of 462 m in the south part of the area, whereas granitoid rocks are weathered flat with a subdued, undulating topography locally broken by kopjes. This is represented by the range and low hills. Outcrop is extremely good, except in the north where the Carlindi Granitoid Complex is extensively covered by an alluvial-colluvial plain (fig. 3.2 and fig. 3.3) (Van Kranendonk, 2000).

3.1.1. Geology

Geological studies of the area are used the name of Strelley belt by Vearncombe (1996), the North Shaw by Van Kranendonk (2000), and Panorama district by Brauhart et al., (1998; 1999 and 2001). The geology of the Panorama area comprises a large granodiorite-granite pluton, intruding tholeiitic to calc-alkaline intermediate to felsic volcanic rocks, overlain by siliceous laminites (silica-carbonate rocks). Outstanding exposures across the Panorama district of the Archean Pilbara block reveal a cross-section of a massive sulphide-bearing volcanic pile and an underlying coeval subvolcanic intru-

Figure 3.1. Schematic location of the Panorama area, Pilbara block, Western Australia;

Figure 3.2. Physiography of Panorama: typical rugged terrain underlying by green-stones.


17

sion, in an area of low metamorphic grade and very low strain (Vearncombe, 1996; Brauhart et al., 1998). The simplified geology is shown in figure 3.3. The Archean lithostratigraphy of the Panorama district is classified into 6 groups, which are Fortescue group, De Grey group, Gorge Creek group, Sulphur Springs group, Warrawoona group and Coonterunah group (Van Kranendonk, 2000). Also granitoid complexes and Cenozoic deposits are described here: Coonterunah group is dominated by tholeiitic mafic volcanic rocks and distributed in the north edge of the area. Warrawoona group occurs in north and south part of the area and composed of pillow tholeiitic ba-salt, chert, komatiite, quartzite, felsic lavas, tuffs, agglomerate, BIF (banded iron formation) and pe-lite. Generally beside to this research, the Warrawoona Group of rocks in the Archean Pilbara Craton con-tains volcanic and sedimentary rocks in which the oldest stromatolities (3.45 Ga) in the world have been discovered. A stromatolitic horizon lies on a chert ridge and in partly silicified laminated car-bonate rock (Grey et al., 2002).

Figure 3.3. The simplified geology map of the Panorama district and VMS deposits where SS - Sul-phur spring, KC – Kangaroo Caves, BE – Bernts, BK – Breakers, MW – Man O’War. Sulphur Springs group is the footwall of VMS deposits, and is distributed surrounding the northern east, east and southern east side of the Strelley granite. The group is composed of differentiated vol-canic-volcanoclastic pile of mainly tholeiitic magmatic affinity varying from basalt to rhyolite, with

Projection: UTM zone 50. Datum: Australian Geodetic 1966.


18

cogmagmatic granite, chert, local polymict megabreccia and iron formation, sandstone, mudstone, quartzite and interbedded metapelite. Gorge Creek group is distributed widely in the hanging wall of VMS deposits. The group is com-posed of Fe-shale, BIF, interbedded sandstone, felsic volcanic rocks, mudstone, minor conglomerate, locally silicified to gray and white chert and basalt. De Grey group is covered the Gorge Creek group in northern east part of the area and is consisted of conglomerate, sandstone and minor shale. Fortescue group occurs in small area of south part and composed of basalt, agglomerate, shale, sand-stone, and andesitic tuff and felsic tuff. Strelley Granite, the synvolcanic, is genetically related to the Kangaroo Caves Formation (Brauhart, 1999; Van Kranendonk, 2000) of the Sulphur Spring group. Three compositionally and texturally phases formed in a synvolcanic laccolith. Three textural phases include: the main outer phase of me-dium-grained, equigranular to weakly porphyritic hornblende-biotite monzogranite; an outermost rind of microcrystalline granophyre at the top of the granite, differentiated from the outer phase; and a younger inner phase of commonly porphyritic hornblende-biotite monzogranite. Carlindi Granitoid complex occurs shortly only in north part of the area and is composed of homoge-neous, leucocratic, equigranular biotite-hornblende granodiorite-granite. Cenozoic geology: Obviosly cenozoic sediments cover much of geological formations and mainly consist of sandy materials dominating quartz or silica. Thus it has a strong influence for emiittance of surface materials. Dissected, consolidated colluvium derived from adjacent rock outcrops is deposited in small areas over much of North Shaw (Panorama). Composed of clay, silt, and sand, it is most widely deposited on flat granitoid complexes and on low slopes or flat plains derived through erosion of topographi-cally high points. Unconsolidated, colluvial sand, silt, and gravel formed on outwash fans and on scree and talus slopes in small pockets across the rugged greenstone terrain of North Shaw. Quartz-feldspathic eluvial sand, with quartz and rock fragments, overlies, and was derived from, granitoid rocks in areas with low slopes. Rivers and creeks contain unconsolidated silt, sand, coarse sand, and gravel. In the larger streams and the Shaw River, these deposits are commonly well sorted, although they may be variable across and along the main drainage channels, with broad sandy areas, gravel and pebble bars (Van Kranendonk, 2000).

3.1.2. Hydrothermal alteration

The surface rock exposures in the Panorama have provided a rare opportunity to map a complete re-gional-scale hydrothermal alteration system associated with VMS and to assess the role of a subvol-canic intrusion in driving such a system (Brauhart et al., 1998). Four major alteration facies can be defined in Panorama district. These are: 1) Feldspar-bearing background alteration resembles akin to spilitic alteration in basalt and kerato-phyric in felsic rocks, is typified by an albite and/or K feldspar-chlorite-calcite/ ankerite-quartz-pyrite assemblage. 2) Feldspar-sericite-quartz alteration commonly characterizes by a K-feldspar and/or albite-sericite-quartz-ankerite-leucoxene±pyrite assemblage. 3) Sericite-quartz alteration is typically a quartz-sericite-leucoxene±ankerite±pyrite assemblage.


19

4) Chlorite-quartz alteration is defined by a quartz-chlorite-sericite± leucoxene±hematite assemblage, in which pyrite±base metal sulphides and ankerite-siderite are only developed immediately beneath zones of base metal mineralization. These alteration facies affect all of the volcanic pile and the upper parts of the underlying subvolcanic intrusion. Alteration facies are not lithology specific but are developed in all lithologies from ande-site-basalt, through rhyolite, to granite (Fig. 3.4).

Figure 3.4. Alteration map and VMS deposits in the research area, Panorama, Australia (Brauhart et al., 1998). Brauhart (1999) did a quantitative assessment of mass transfer during hydrothermal alteration process in Panorama. Variations in mass transfer of SiO2 between different alteration facies are presented for andesite-basalt and granophyric outer phase granite (fig. 3.6) and typify changes across the alteration system. Mass transfer map of SiO2 is presented in Figure 3.5 (Brauhart et al. 2001). He described that, silica is generally enriched in most andesite-basalt, but it is most enriched in feldspar-sericite-quartz alteration. The average chlorite-quartz altered andesite-basalt is slightly enriched in silica with respect to the average background alteration. Sericite-quartz altered granophyric-granites have generally un-dergone silica mass gain, but most background and chlorite-quartz altered granophyric-granites have undergone little change. Some chlorite-quartz altered granophyric granites have undergone significant silica depletion, but the average mass transfer of silica is close to zero. There is a blanket of silica enrichment at the top of the volcanic pile, corresponding to the zone of feldspar-sericite-quartz alteration, and andesite-basalt within this zone contain up to 75% SiO2 (Brau-hart et al. 2001). Most of the granite and the base of the volcanic pile undergone minimal silica mass transfer, but small size greisens are notably enriched in silica. There is no extensive zone of silica de-pletion to account for the silica enrichment at the top of the volcanic pile. Assuming that seawater did


20

not add substantial amounts of silica to the hydrothermal system, then this silica likely came from the base of the volcanic pile and/or from the granite. A slight adjustment of the least-altered fractionation curves for silica would yield modest silica depletion in the Strelley Granite and the base of the vol-canic pile, and hence potentially account for the silica enrichment at the top of the volcanic pile (Brauhart, 1999).

Andesite-basalt Granophyre

bg fse trans-cq cq-cb cq bq se cq

3.1.3. VMS deposits and silica

Five of the main Zn-Cu VMS prospects (Sulphur spring, Kangaroo Caves, Breakers, Man O’War and Anomaly 45; fig. 3.3; 3.4 and 3.5) are near the top of the Sulphur Springs Group, spaced at regular 5-7 km intervals around the Strelley Granite. A sixth prospect, the Bernts prospect, forms at the same stratigraphic level but within the fault-bounded Bernts deformation zone (Van Kranendonk, 2000). VMS deposits are sited within, or immediately below, a regionally extensive unit of strongly silicified siltstone at the contact between the Strelley succession and the Gorge Creek Group. This favorable horizon, informally termed the “Marker Chert”, is typically less than 5 m thick, but may be up to 80 m thick near the base metal mineralization (Brauhart, et, al., 1998). Vearncombe et al. (1998) described it by the term of siliceous laminitis, which rest directly on the footwall volcanic rocks, and reach a thickness of 50 m (at Sulphur Springs) in the immediate area of massive sulfide mineralization, but otherwise are only 10-20 m in thickness. The siliceous laminitis consist principally of interbedded cryptocrystalline to microcrystalline quartz, vein quartz, fine silty-sandy laminate and rhomboid (siderite) carbonate. Concordant and crosscutting vein quartz makes up a large but unquantifiable proportion of the siliceous laminitis. The siliceous laminitis is interpreted as exhalative and sedimentary products, common to VMS systems, although poor in Fe relative to

Figure 3.5. Mass transfer map of SiO2 (wt %). Where dark red represents the highest mass gain (Brauhart et al, 2001). Figure 3.6. Mass transfer diagram of SiO2 (wt %) for ande-site-basalt and granophyric granite, showing the average, standard deviation (ticks), and range (outer circles) of SiO2 values for each alteration facies (Brauhart et al. 2001), where bg – background, fse – feldspar-sericite-quartz, trans-cq – transitional chlorite-quartz, cq-cb – chlorite-quartz-carbonate, se – sericite-quartz;


21

equivalents such as the iron-rich cherts or tetsusekiei of the Kuroko-type deposits (Vearncombe et al., 1998). Sulphur Springs (5.3 Mt @ 6.1% Zn and 2.2% Cu) and Kangaroo Caves (1.7 Mt @ 9.8% Zn and 0.6% Cu) are the largest VMS deposits in the Panorama area (Brauhart, et, al., 1998).

3.2. Dataset and equipment

The main data source for this research is the multispectral scanner, the MODIS/ASTER Airborne Simulator (MASTER) that acquires data over the spectral range 0.4 to 13 µm in 50 bands at 5 to 50 m of spatial resolutions. From these 50 bands, 10 bands are in the TIR region. The TIR bands 42 to 48 used in this study have the 15 m spatial resolution. In order to validate the in-flight performance of the instrument, the Jet Propulsion Laboratory and the University of Arizona conducted a joint experiment in December 1998. The experiment involved overflights of the MASTER instrument at two sites at three elevations (2000, 4000 and 6000m). The two sites Ivanpah Playa, California, and Lake Mead, Nevada, were selected to validate the visible-shortwave infrared and thermal infrared channels, re-spectively. At Lake Mead contact and radiometric surface lake temperatures were measured by buoy-mounted thermistors and self-calibrating radiometers, respectively. Atmospheric profiles of tempera-ture, pressure, and humidity were obtained by launching an atmospheric sounding balloon. The meas-ured surface radiances were then propagated to the at-sensor radiance using radiative transfer models driven by the local atmospheric data. There was excellent agreement between the predicted radiance at sensor and the measured radiance at sensor. The percent difference between the TIR channels not strongly affected by the atmosphere was typically less than 0.5%. Results indicate the MASTER in-strument should provide a well-calibrated instrument for Earth Science Studies (Hook et al. 2001).

Channel Full width half maximum Channel center Channel peak 42 0.4333 8.1677 8.185 43 0.3543 8.6324 8.665 44 0.4253 9.0944 9.104 45 0.4083 9.7004 9.706 46 0.3963 10.116 10.115 47 0.5903 10.6331 10.554 48 0.6518 11.3293 11.365

Table 3.1. Spectral characteristics of the TIR MASTER channels, in µm; Ground data including XRF whole-rock chemistry, geological (1:100000 and district scale) and altera-tion map (district scale), and µFTIR (micro Fourier transform interferometer) laboratory emissivity spectra are used in order to identify silicification zones on the ground and evaluate the remote detec-tion and identification. A total of 445 and 44 rock samples were analysed in the XRF whole-rock geo-chemical analysis and µFTIR spectral laboratory respectively. The Envi 3.5, ILWIS 3.11, ArcGIS, R (a programming environment for data analysis and graphics), SPSS 10, Paint Shop pro and Microsoft Windows 2000 Professional system were available for analy-sis of all datasets in Intel ® Pentium 4 CPU provided by ITC.


22

Chapter 4. Methodology

4.1. Research scheme

All inputs in the study are grouped into two main parts called ground data and airborne data. These data are processed separately using corresponding methods, then compared to each other and finally results, which are derived from airborne data, are validated using ground data. The detail flowchart for this study is shown in figure 4.1.

Ground data MASTER TIR data

Map silicificationGeostatistics & GIS

Approximateemissivity

Emissivityimages

Evaluation

Geology map Rockchemistry

emissivityspectra

Decorrelationstretch Spectral ratio

Map silicificationComparison

Figure 4.1. The detail flowchart of the research methodology;

4.2. Methods

Methods are used for analysis here including exploratory data analysis, point interpolation methods, decorrelation stretching, spectral band ratio, emissivity normalization, reference channel emissivity, alpha residuals and reference point calibration method. Some of these methods were previously dis-cussed in chapter 2.

4.2.1. Exploratory data analysis

Under the subtitle of exploratory data analysis (EDA), general statistics are applied to examine varia-tions in data including univariate and bi-variate EDA for this research. Univariate EDA is composed of boxplot, histogram and five-number summary (min, 1st quartile, median, 3rd quartile, max), and is considered on questions of population, outliers, centered or skewed (mean vs. median), and “heavy” or “light” tails (kurtosis). Bi-variate EDA is analyzed relation between two variables using scatterplot etc. and also questions of multiple populations and outliers.


23

4.2.2. Point interpolation methods

A point interpolation performs an interpolation on randomly distributed point values and returns regu-larly distributed point values. This is also known as gridding. In ILWIS, the output values are raster values. The input map is a point map in which the points themselves are values (point map with a value domain) of SiO2/Al2O3 ratio, for instance concentration values of silicification process. The output of a point interpolation is a raster map, which represents silicification. For each pixel in the output map, a value is calculated by an interpolation on input point values. Brauhart et al. (2001) published estimation of spatial variations in mass transfer using an inverse dis-tance squared weighting routine, where the value for each cell is based on the weighted value of the nearest 12 samples (fig. 3.5). This type of calculation is used in ILWIS is the moving average interpo-lation method, which assigns to pixels weighted averaged point values. The weight factors for the points are calculated by a user-specified weight function. Weights may for instance approximately equal the inverse distance to an output pixel. The weight function ensures that points close to an out-put pixel obtain larger weights than points, which are farther away. Furthermore, the weight functions are implemented in such a way that points, which are farther away from an output pixel than a user-defined limiting distance, obtain weight zero (ILWIS 3.1). Also the ordinary kriging method was used. The estimations of kriging are weighted averaged input point values, similar to the moving average operation. The weight factors in kriging are determined by using a user-specified semi-variogram model (based on the output of the spatial correlation opera-tion), the distribution of input points, and are calculated in such a way that they minimize the estima-tion error in each output pixel. The estimated or predicted values are thus a linear combination of the input values and have a minimum estimation error. The optional error map contains the standard er-rors of the estimates. In ordinary kriging, one can influence the number of points that should be taken into account in the calculation of an output pixel value by specifying a limiting distance and a mini-mum and maximum number of points (ILWIS 3.1).

4.2.3. Decorrelation stretching

The Decorrelation contrast stretching is an effective method for displaying information from multispectral TIR images (Gillespie, 1992). The Decorrelation stretching removes the high correlation commonly found in multispectral datasets and produces a more colorful color composite image. The highly correlated data sets often produce quite bland color images. Decorrelation stretching requires three bands for input. These bands should be stretched byte data or may be selected from an open color display. A result will represent variations in surface emissivity by hue and surface temperature by intensity. Decorrelation stretching images retain a surface temperature component that reflect the topography of an area and yield qualitative information relating to the nature of exposures and rock compositions (Sabine et al. 1994). Decorrelation stretching is accomplished in four steps:

- In the first step, the covariance matrix is determined for the image and the eigenvectors are calculated.

- In the second step, the image is actually transformed from the radiance domain to the PC (Principal Component) space. The PC is a special linear transformation (rotation and transla-tion) of the radiance space, but the transformed data have the unique property that they are statistically independent or “decorrelated”.


24

- In the third step, the PC images are separately contrast-stretched, generally to equalize the variances of the three or more images with the highest signal-to-noise ratios.

- In the fourth step, the inverse transformation is calculated that would rotate the unstretched PC images back to the original radiance space, and this transformation is applied to the stretched data. Even though the data are retransformed to the radiance space, the covariance of the enhanced images is reduced. Three of the retransformed image channels are displayed as a false-color picture (Gillespie, 1992).

After producing the decorrelation stretched image, a hue wiping is applied for represent variations in surface emissivity using Paint Shop pro.

4.2.4. Spectral band ratio method

The method divides pixels in one image by the corresponding pixels in a second image. Dividing one spectral band by another produces an image that provides relative band intensities. The image en-hances the spectral differences between bands. Ratios clearly portray the variations of slopes of spec-tral curves between two bands involved and are independent of the absolute pixel values. One may combine three ratios into a color-ratio-composite (CRC) image to determine the approximate spectral shape for each pixel's spectrum. Using Envi 3.5 to calculate band ratios, one must enter a "Numerator" band and a "Denominator band." The band ratio is the numerator divided by the de-nominator. ENVI checks for division-by-zero errors and sets them to 0. The color-ratio-composite image of band ratios (47/46 – red, 45/44 – green, 44/43 – blue) is computed in Envi 3.5. The accuracy of the spectral ratio method depends on spectral bandwidths. In this case the MASTER instrument has relatively narrow spectral bandwidths (band 43 – 0.35; 44 – 0.42; 45 – 0.4; 46 – 0.39; 47 – 0.59 µm).

4.2.5. Emissivity normalization

This method was first described by Gillespie (1985) and used by Realmuto (1990) and Gillespie et al. (1998 and 1999). The emissivity normalization technique calculates the temperature for every pixel and band in the data using a fixed emissivity value. The highest temperature for each pixel is used to calculate the emissivity values using the Planck function (in chapter 1 and 2). One has to enter the desired fixed emissivity value. The assumed emissivity value is 0.96, which represents a reasonable average of likely values for exposed geologic surfaces (Kealy et al., 1993).

4.2.6. Reference channel emissivity

The reference channel emissivity technique assumes that all the pixels in one channel (band) of the TIR data have a constant emissivity. Using this constant emissivity, a temperature image is calculated and those temperatures are used to calculate the emissivity values in all the other bands using the Planck function. For the MASTER TIR data, the channel 48 is assigned to a constant emissivity of 0.949. The central wavelength of the channel 48 is 11.329 µm, which is close to the center (11.655 µm) of the TIMS channel 6. According to the Kealy and Hook (1993), the value of 0.95 is chosen since it represents an average emissivity value for silicate rocks in the wavelength region of the TIMS channel 6. The wavelength region covered by this channel exhibits the least variation in emissivity. Also 0.949 is the average of the laboratory emissivity in the 11.329 µm wavelength measured by the µFTIR instrument on rock samples collected from the Panorama. When the emissivity in the reference


25

channel is known, we can isolate and determine a temperature for that channel by equation 4 (in the chapter 2).

4.2.7. Alpha residuals method

The alpha residuals method was developed Kealy and Gabell (1990) and Hook et al. (1992). Alpha residuals method is to produce alpha residual spectra that approximate the shape of emissivity spectra from TIR radiance data. This approach utilizes Wien's approximation of the Planck function:

Bλ(T)=C1λ-5π[exp (C2/λT)]-1 (8) Wien's approximation neglects the -1 term, making it possible to linearize the approximation with logarithms (Kealy and Hook, 1993). The temperature and emissivity terms are separated and means are used to subtract the temperature term out. The alpha residual spectra are a function of emissivity only and have a similar shape as emissivity spectra but have a zero mean. Taking natural logs of the surface radiance using Wien’s approximation we obtain (Kealy and Hook, 1993):

TC

CLj

jjj λπλε 2

1 lnln5lnlnln −−−+= (9)

where: Lj – blackbody radiance (Wm-3), λ j – wavelength of channel j (m), T – temperature of the blackbody (K), C1 – first radiation constant =3.74151*10-16 (Wm2), C2 – second radiation constant =0.0143879 (mK). Equation (9) is then multiplied by λ j in order to separate the λ and T terms:

TC

CL jjjjjjjj2

1 lnln5lnlnln −−−+= πλλλλελλ (10)

Calculating the mean of the equation set for j=1, N where N is the number of channels, we obtain:

TC

NNNC

NL

N

N

jjj

N

jj

N

jj

N

jjj

N

jjj

2

111

1

11

lnln

5lnln

1ln

1 −−−+= =====

λπλλλελλ (11)

The element containing the temperature term has no summation sign associated with it because it is invariant over the N equations. Subtracting (11) from (10)

==

===

+−+

−−+−=−

N

jjjj

N

jj

jj

N

jjj

N

jjjjj

N

jjjjj

NN

NC

CN

LN

L

11

1

11

11

lnlnln

5

ln5ln

lnln1

lnln1

ln

λππλλλ

λλλλελελλλ (12)

and putting the elements containing ε to the left-hand side,


26

==

===

−+−

+−−−=−

N

jjjj

N

jj

jj

N

jjj

N

jjjjj

N

jjjjj

NN

NC

CLN

LN

11

1

11

11

lnlnln

5

ln5ln

lnln1

lnln1

ln

λππλλλ

λλλλλλελελ (13)

N similar equations are created each without a T term. A new parameter αj, is then defined:

j

N

jjjjjj

N

jjjjj KL

NL

N+−==−

== 11

ln1

lnln1

ln λλαελελ (14)

where Kj contains only terms, which do not include the measured radiances, Lj, and hence, may be calculated from the constants. The set of N values of αj can be calculated, using the right-hand side of (14) by using the ground radi-ance data. These αj are temperature independent, containing no T term. Using laboratory derived emissivity spectra, with the left-hand side of (14) it is possible to calculate αj. Thus, direct compari-son of ground α spectra to laboratory α spectra is possible. An extension of the aforementioned method allows ε to be calculated. The left-hand side of (14) may be rearranged such that

��

�

�

��

�

� +=

=

j

N

jjjj

j

N

λ

ελαε 1

ln1

exp (15)

However, this is not solvable since the value of the mean in (15), is not known. An estimation of this value is needed to solve for ε (Kealy and Hook, 1993).

4.2.8. Reference point calibration method

Emissivity images derived from above mentioned methods were corrected for atmospheric effects by the reference point calibration method. Rock sample spectra were used for this calibration. The method (Li et al. 1999) is based on the linear correlation between relative emissivities (Ec) de-rived from TIMS data using radiosonde data and relative emissivities derived with the modified at-mospheric profiles (fig. 4.2) and also the assumption that there is no spatial variation in the atmos-pheric conditions over the study region in the image. Li et al (1999) described that if the observations are taken on the ground level and if the values of E (relative emissivity) for reference points are known a priori from the field measurements or from the other ways, the offset of the linear relationship between Ec and E is b which can be obtained by:

b=(Ec-E)/(E-1) (16)

and the slope is 1+b. Noting that the E value of reference point cannot be close to unity. After knowing b, the value of E for other points in the images can be easily derived by equation:

E=(Ec+b)/(1+b) (17)


27

4.3. Comparison and validation

Spectral emissivities were directly measured in the laboratory using a µFTIR instrument on 44 sam-ples collected from the Panorama area. Emissivity spectra of these measurements are compared with airborne MASTER TIR original data on the spectral shape. Before comparison the laboratory emissiv-ity spectra is resampled using the channel center wavelengths and full width half maximum (FWHM) of MASTER TIR data. Emissivity images created by three methods including emissivity normalization, reference channel emissivity and alpha residuals were compared with laboratory spectra. Then emissivity images cor-rected from atmospheric effects using reference point calibration method were also compared with laboratory spectra. The silicification map expressed by emissivity images is validated using the whole-rock geochemistry and interpolated silicification map from ground data. This task is solved technically by cross opera-tion and estimation of correlation between those values in ILWIS. The correlation coefficient was cal-culated according to the following formula:

zy SScov=ρ (18)

where: cov – covariance, Sy and Sz – standard deviation of values. Covariance:

=

−−−

=n

iii zzyy

n 1

))((1

1cov (19)

Standard deviation, where x - average:

=

−−

=n

ii xx

nS

1

2)(1

1 (20)

Also a visual comparison is applied for qualitative validation. Overlying VMS deposits on the silicifi-cation map visualizes the spatial relationship between the silicification and VMS deposits.

Figure 4.2. Illustration of linear rela-tionship between Ec derived from TIMS data with radiosonde data (water vapor content W=Wr=4.32 g/cm2) and Ec derived with deviate atmospheres (W=0.6 Wr and W=0.8 Wr) for two view angles (nadir and 300) (from Li et al. 1999).


28

Chapter 5. Silicification from ground data

5.1. Siliceous geological map units

Siliceous geological units are described from geological maps of Brauhart et al. (1998) and Van Kra-nendonk (2000). The district scale geology and alteration map of Brauhart et al. (1998) are used to map silicification due to the distribution extent of sample locations of whole-rock chemistry and labo-ratory rock emissivity. The geology map of Van Kranendonk (2000) at scale 1:100 000 (in appendix) is the useful data for geological units and descriptions, additionally for Cenozoic sediments and is covered the full extent of the airborne MASTER imagery. Silicification is an alteration type, which is best described by geochemistry intrinsically. A geology map can present siliceous geological units of geological and hydrothermal processes, including chert (silica) layers, cherty iron formation, chert-barite dykes, silicified sandstone and shales, gossan (with silica veinlets), quartz veins, quartz arenite, sandstone, felsic igneous rocks and Cenozoic sandy sedi-ments (geology map in appendix).

5.1.1. Coonterunah Group

5.1.1.1. Coucal Formation (AOcbi, AOcf, AOci)

The base and top of the Coucal Formation mark by one, two, or three 2-10 m thick beds of centimetre-layered, black and white banded cherty iron-formation (AOci). The beds vary from evenly layered rocks with 1-2 cm thick black and white layers, to millimetre-centimetre layered black, white, and less common red layers, to solid black rock with sparse, irregularly spaced, white chert layers. Felsic volcanic rocks (AOcf) include massive, highly amygdaloidal dacite and subordinate dacite por-phyry, and hyaloclastite and pumiceous rhyolite. The rocks, affected by metamorphic recrystallization and carbonate-sericite alteration, are commonly fine grained in thin section.

5.1.2. Warrawoona Group

5.1.2.1. Strelley Pool Chert (AWs)

The Strelley Pool Chert (AWs) was the name given to a unit of silicified carbonate and siliciclastic rocks in the East Strelley Belt, where it lies disconformable to unconformable on the Coonterunah Group. Results of current mapping have shown that the Strelley Pool Chert also conformably to dis-conformably overlies the Panorama Formation in the Panorama and North Shaw Belts. The Strelley Pool Chert is a characteristically white-to-white and grey-layered chert that forms a steeply dipping ridge rising above the surrounding volcanic rocks. Silicification of the Strelley Pool Chert is locally a recent feature, as indicated by the fact that where topographically high ridges of pure chert are transacted by a stream, the rocks pass along strike, down topographically into pure brown carbonates, which display the identical textural features as their silicified counterparts higher up.


29

5.1.2.2. Euro Basalt (AWec, AWeft)

The Euro Basalt lies conformably above the Strelley Pool Chert in the East Strelley, Panorama, and North Shaw Belts. The unit is principally composed of pillowed basalt of interbedded tholeiitic and high-Mg composition, and higher up contains thin beds of chert (AWec), and felsic volcaniclastic rocks (AWeft). The felsic volcaniclastic unit is composed of feldspathic sandstone with cross-bedding, shale, finely ripped ash beds, volcaniclastic sandstone and agglomerate, pebble conglomerate, and chert (silicified felsic ash?). South of the Shaw River, the unit consists of thinly bedded, grey, tuf-faceous chert (silicified felsic ash) with beds of accretionary lapilli, volcaniclastic sandstone, and pebble conglomerate. At the top of the Euro Basalt, in the southwestern extension of the Panorama Belt west of the Shaw River, is an approximately 2800 m thick sequence of interlayered pillow basalt and cherts of varied character. A distinctive unit of chert, up to 40 m thick, is layered on a 1-5 cm scale between alternating white (chert) and black (hematite) layers. The rock is recrystallized. Other cherts in this part of the formation include massive blue-black chert, pale-brown lithic siltstones, and white and gray-layered chert.

5.1.3. Sulphur Springs Group

5.1.3.1. Six Mile Creek Formation (ASmc)

The main outcrop of the Six Mile Creek Formation north of the Strelley Granite consists of massive high-Mg basalt, pillowed basalt, and chert (ASmc). Bright, light-green chert forms in the northwest fine-grained siliciclastic rocks. Pillow basalts are interbedded with both grey, aphanitic cherts that represent silicified flow tops, and with less common, silicified, grey-green siltstones (ASmc).

5.1.3.2. Leilira Formation (ASls, ASlf)

The Leilira formation situates narrow bands in the southern corner of the study area. It is composed of interbedded lithic arenite, wacke, quartz sandstone (ASls), and felsic volcanic rocks (ASlf). Rocks are typically sandstones, but locally vary from coarse to pebbly sand and local siltstones. Bedding is com-monly not well developed, and identified principally by trends on airphotos. These siliciclastic rocks contain abundant clasts of felsic volcanic material, mafic volcanic detritus, and milky white to clear quartz.

5.1.3.3. Kunagunarrina Formation (ASks, ASkcw)

Outcrops of the Kunagunarrina formation distribute in the north part and compose lower unit of the volcanic pile respectively. Creamy-coloured, thin-bedded shale and siltstones (ASks) are near the top of the formation. Centimetre-thick sills of black and grey chert, and these increase intrude the top of this unit upwards until the rock is a well-layered black and white chert (ASkcw).

5.1.3.4. Kangaroo Caves Formation (AScbi, AScfd, AScfr, AScc, ASci, AScsx)

The Kangaroo Caves Formation composes the volcanic pile and it is up to 1.5 km thick. From ap-proximately bottom to top, the formation is composed have massive, pillowed-brecciated, and hyalo-clastic andesitic volcanic rocks (andesite-basalt and andesite), and sills and lavas of dacite, rhyo-dacite, and rhyolite. Andesitic volcanic rocks are much more abundant in the northern part of the vol-canic pile, whereas felsic compositions are more common in the south. West of the Sulphur Springs deposit, spherulitic rhyodacite sills are common, whereas between Sulphur Springs and Kangaroo


30

Caves, dacite sills (AScfd) that are variably amygdaloidal, typically aphyric, and commonly perlitic, have been emplaced near the top of the formation. Individual sills reach 250 m in thickness, and may be continuous for several kilometres along strike. South of Kangaroo Caves, rhyolite domes and pum-iceous volcaniclastic rocks (AScfr) increase in abundance. The bulk of the volcanic succession around the Stelley Granite is underlain by andesite-basalt (AScbi). This unit varies from pillowed to massive and is variably amygdaloidal. Hyaloclastite is commonly developed between pillows, at flow tops, and in some thicker sequences. The primary mineralogy of this unit is not preserved. The marker chert at the top of the Sulphur Springs Group (AScc) is up to 100 m thick. The chert is typically composed of centimetre-layered, grey-blue and white, silicified, and fine-grained volcani-clastic (including andesite shard-rich sandstone) and epiclastic (including black mudstone, sandstone, and breccia) detritus intruded by discordant veins, and concordant sills of white chert and black, kero-genous chert. Local quartz sandstone and banded iron-formation occur in part of this unit southeast of the Strelley Granite. At Sulphur Springs, the marker chert overlies by a unit of polymict megabreccia (AScsx) across a sharp, erosion contact. The megabreccia is up to 600 m thick and is composed of blocks and frag-mented rafts of felsic volcanic rocks, silicified fine-grained sediment (chert), banded iron-formation (ASci). Within the upper part of the megabreccia is a unit of red, black, and white layered, ferruginized silt-stone and banded iron-formation (ASci). At Kangaroo Caves, feldspar-phyric, calc-alkaline rhyodacite (AScfr) forms an elongate dome, 400 m high by 1500 m wide, above the marker chert and its associated mineralization. The dome is capped by another bed of silicified siltstone (chert), which is unmineralized.

5.1.3.5. Strelley Granite (Agste, Agstey, Agstp)

The Strelley Granite is described in the chapter 3. Here mentioned again only names of three phases: the main outer phase of medium-grained, equigranular to weakly porphyritic hornblende-biotite mon-zogranite (Agste); an outermost rind of microcrystalline granophyre (Agstey) at the top of the granite, differentiated from the outer phase; and a younger inner phase of commonly porphyritic hornblende-biotite monzogranite (Agstp) (Van Kranendonk, 2000). Both the outer and inner phases contain miaro-litic cavities, and the original mineralogy of these rocks is extensively altered to chlorite-sericite (Brauhart, 1999).

5.1.4. Gorge Creek Group

5.1.4.1. Corboy Formation (AGcc, AGct, AGcb)

Thick-bedded turbidite sandstones of the Corboy Formation (AGct) form the base of the Gorge Creek Group, where they onlap the top marker chert of the Sulphur Springs Group around the northeastern end of the Strelley Granite. The formation is up to 475 m thick in this area, but has a lensoid shape, thinning out along strike to the north and west over the top of the olistostrome breccia at the top of the Sulphur Springs Group (AScsx), and also thinning to the south over the top of the felsic volcanic dome at the Kangaroo Caves prospect. The Corboy Formation is also on the western side of the breccia as a less than 100 m thick unit of coarse sandstone. Immediately above the thin eastern wedge of the olis-tostrome breccia, the Corboy Formation includes a basal matrix-supported conglomerate (AGcc), up


31

to 30 m thick, which is interlayered with lenses and beds of coarse sandstone. The conglomerate con-tains subrounded cobbles and pebbles of layered black chert derived from the underlying marker chert bed. The Corboy Formation also drapes over the felsic volcanic dome at the Kangaroo Caves prospect. At this locality, a thin, basal unit of pebbly conglomerate along the flanks of the dome displays excellent cross bedding. Further south within the study area, the Corboy Formation is predominantly composed of sandstones and lesser amounts of interbedded siltstones. North and south of the Bernts deposit prospect, foliated sandstones and siltstones are tectonically in-terleaved with mafic and ultramafic schist within the Bernts Deformation Zone. The scale of interlay-ering is too fine to show at the map scale and thus the various rock types have been lumped together (AGcb).

5.1.4.2. Paddy Market Formation (AGphc, AGpi, AGph)

In the north of the study area, the formation consists of thinly bedded, black, white, red, and grey cherty banded iron-formation and minor ferruginous shale (AGpi). This passes along strike to the east and gradationally above the Corboy Formation into ferruginous shale and mudstone with local silt-stone and sandstone (AGph). Also here near to the main fault zone, tightly folded, centimetre-bedded, grey and white, layered chert after shale (AGphc) occurs.

5.1.4.3. Pyramid Hill Formation (AGyhc, AGyi)

In the northern east of the area, shales are commonly silicified to centimetre-layered, blue-black and white cherts (AGyhc). Two observations suggest that this silicification predated deposition of the un-conformable overlying Lalla Rookh Sandstone (Van Kranendonk, 2000). Banded iron-formation (AGyi) forms the top of the Gorge Creek Group in the eastern part of the area. These rocks are dark red-brown in color and well layered at a 2-5 cm scale, with alternations of hema-tite-rich and cherty beds.

5.1.5. De Grey Group

5.1.5.1. Lalla Rookh Sandstone (ADls, ADlc)

The Lalla Rookh Sandstone consists of matrix-supported pebble to boulder conglomerate and diamic-tite (ADlc), and pebbly sandstone, quartzose arenites, feldspathic sandstone, and minor shale (ADls). Conglomerate clasts include primarily black chert, layered black and white, or black and grey chert, banded iron-formation, white quartz, various metasandstones, rare felsic metavolcanic rocks, and very rare pebbles of mafic-ultramafic schist.

5.1.6. Carlindi Granitoid Complex (AgLg)

The Carlindi Granitoid Complex is underlain by homogeneous, leucocratic, equigranular biotite-hornblende granodiorite-granite (AgLg). The rock is composed of 5% of biotite-epidote-titanite-muscovite-actinolite clots in a coarse-grained matrix of quartz-plagioclase-K-feldspar (microcline and antiperthite).


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5.1.7. Unassigned rocks (Ac, Aq, go)

Unassigned cherts (Ac) are occurred shortly in the north side of the area. A massive blue-black chert, centimetre-layered, white, aphanitic gray, and green cherts are present. Massive, white quartz veins (Aq) are present within faults cutting the Strelley Granite. Surface gossan (go) is exposed at the Sulphur Springs prospect. The gossan consists of a dark red-brown weathering, massive rock with a deeply pitted, irregular surface. Broken samples are porous and white in colour, commonly consisting of a fine, reticulated network of silica veinlets, interstitial porosity, and dark-purple, centimetre-wide veins of hematite (-magnetite).

5.1.8. Fortescue Group

5.1.8.1. Hardey Formation (AFhs)

The Hardey Formation consists of clastic sedimentary rocks including, from base to top, boulder to pebble conglomerate and local interbedded sandstone, shale, and overlying quartz arenite (AFhs). Sandstone is the most common rock type in this formation, consisting of well-sorted detritus and dis-playing local cross bedding.

5.1.9. Cenozoic Geology

On the Carlindi Granitoid Complex, a ferruginous silt and sand (Czcf) blanket covers large areas of flat ground. Dissected, consolidated colluvium (Czc) derived from adjacent rock outcrop is deposited in small ar-eas over much of North Shaw (Panorama). Composed of clay, silt, and sand, it is most widely depos-ited on flat granitoid complexes and on low slopes or flat plains derived through erosion of topog-raphically high points. A specific variety of variably consolidated and dissected colluvium (Czcg) is composed of quartz-feldspar clay, silt, sand, and gravel derived proximally from, and deposited on, the Strelley granite complex. Unconsolidated, colluvial sand, silt, and gravel (Qc) formed on outwash fans and on scree and talus slopes in small pockets across the rugged greenstone terrain of North Shaw. Rivers and creeks contain unconsolidated silt, sand, coarse sand, and gravel (Qaa). In the larger streams and the Shaw River, these deposits are commonly well sorted, although they may be variable across and along the main drainage channels, with broad sandy areas, gravel and pebble bars, and broad pebbly washes. The active stream channels are typically bound by lines of snappy gums, be-yond which are marginal, overbank deposits (Qao) partly stabilized by sparse undergrowth, which consist of alluvial sand, silt, and gravel. These deposits commonly form on flat floodplains adjacent to main drainage channels at bends in the streams, or on low islands within the stream (Van Kranen-donk, 2000). Different types of cherts (silica) including chert layers, chert-barite, cherty-iron formation and quartz vein are displayed in figure 5.1.


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5.2. Whole-rock geochemistry

The whole-rock geochemistry data analysed by XRF has 445 samples collected from the Panorama, mostly from the volcanic pile (fig. 5.2), and 349 of them are volcanic rocks. Samples were collected across the strike and covered full of the volcanic pile to determine a compositional behaviour of it.

Figure 5.1. All types of cherts in-cluding, chert-barite, cherty-iron formations and quartz vein in the Panorama area; white points are VMS deposits; Diagonal border is the extent of airborne MASTER imagery;

Figure 5.2. The simplified ge-ology of the Panorama district and sample points of whole-rock geochemistry; Relatively few samples are from granite rocks.


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5.2.1. Exploratory data analysis

SiO2 content is represented by percentage in a rock sample. Summary statistics of SiO2 content: Min. 1st Qu. Median Mean 3rd Qu. Max. 39.07 58.58 66.00 66.52 75.79 91.61

Figure 5.3. The histogram and normal Q-Q plot of SiO2 content. The histogram of SiO2 content (fig. 5.3), it represents two population subdivided by 75% SiO2. The estimation of coefficient of variation (CV) by formula:

CV=(standard deviation)/mean => (0.14= 9.610487/66.52) represents a normal distribution, because when CV is less than 0.5, is associated to normal distribu-tion. But if we look at the normal quantile-quantile (Q-Q) plot, the distribution of SiO2 content is not exactly normal, is a two-sided one and also it shows two populations classified by a relatively sharp folding around 75 % (fig. 5.3). SiO2/Al2O3 ratio is chosen for representing silicification in the volcanic pile. Summary statistics of the ratio: Min. 1st Qu. Median Mean 3rd Qu. Max. 3.360 4.990 5.719 6.115 6.861 32.710

Figure 5.4. The histogram and boxplot of the SiO2/Al2O3 ratio;


35

The exploratory data analysis of SiO2/Al2O3 ratio, including histogram and boxplot, shows main popu-lation from 3.36 to 10, anomalous part of few samples from 10 to 15, and two extreme outliers of 26.9 and 32.71 (fig. 5.4 and 5.5). One (26.9) of two outliers is situated in the outer phase intrusive rocks and the other one (32.71) is within the most crowded samples in the north part of the volcanics (see fig. 5.2). So an influence of outlier for interpolation in the volcanics became weak due to dense sam-pling in this part. In the volcanic pile, the correlation between the SiO2/Al2O3 ratio and SiO2 content is 0.66, which seems that two values are not highly correlated to each other. The scatter graph between the SiO2/Al2O3 ratio and SiO2 content in the volcanic pile displays the positive relationship between them and most of samples have the SiO2/Al2O3 ratio value below 15 (fig. 5.5).

0 5 10 15 20 25 30SiO2/Al2O3

45

50

55

60

65

70

75

80

85

90

SiO

2

5.2.2. Point interpolation and classification

Prior to interpolate silicification it is necessary to separate volcanic samples (fig. 5.2) from granite samples as geological evidence (Brauhart et al., 2001) suggests that the granite intruded after the vol-canic pile was completely (or nearly so) in place. Interpolation methods in ILWIS_3.11 including moving average and ordinary kriging were applied. From these two interpolations the inverse distance moving average (fig. 5.6 a) method gave the better result than ordinary kriging (fig. 5.6 b). Therefore only the moving average method is further dis-cussed, which is calculated using a pixel size of 15m. Visual comparison of the mass transfer map of SiO2 (wt %) of Brauhart et al. (2001) (fig. 5.7 a) to the silicification map in volcanics represented by SiO2/Al2O3, (fig. 5.7 b) reveals that both are similar to each other. In the interpolated silicification map there is one red spot on the north side of the volcan-ics or just below the Sulphur Springs deposit that is derived from the outlier value of 32.71 (fig. 5.6 a). The interpolated silicification map indicates result of convective hydrothermal processes, which were keen to form VMS deposits at Panorama. The Panorama VMS hydrothermal alteration system has interpreted as a convective hydrothermal system by Brauhart et al. (1998), and this descriptive model is applied in Brauhart et al. (2001).

Figure 5.5. The scatter graph between SiO2/Al2O3

ratio and SiO2 content in the volcanic pile;


36

a) b) Figure 5.6. a) Silicification in volcanics represented by SiO2/Al2O3 ratio, interpolated by the inverse distance moving average method and points show VMS deposits, legend represents the degree of sili-cification; b) Ordinary kriging of SiO2/Al2O3 ratio.

a) b) c) Figure 5.7. Visual comparison of silicification and alteration facies maps;

a) The mass transfer map of SiO2 (wt %), where dark red represents the highest mass gain (Brauhart et al, 2001);

b) The silicification in volcanics represented by SiO2/Al2O3 ratio, which from blue to red repre-sents the increasing trend of silicification and VMS deposit points;

c) The Panorama alteration facies map (Brauhart et al., 1998), where bg – background alteration, cq – chlorite-quartz alteration, cq-c – chlorite-quartz-carbonate alteration, fse – feldspar-sericite-quartz alteration.


37

The silica enrichment is represented at the top of the volcanic pile by interpolated map (fig. 5.6a and 5.7 b). This evidence coincides with previous maps (Fig. 5.7a and 5.7c). Brauhart et al. (2001) noted that feldspar-sericite-quartz altered rocks are restricted to the top of the volcanic pile and, in andesite-basalt, this facies is typified by K and Si enrichment and variable Ca, Na, Mg, and Fe depletion. This silica enrichment blanket at the top of the volcanic pile, corresponding to the zone of feldspar-sericite-quartz alteration, and andesite-basalt samples within this zone contain 75% SiO2 and more. Unlike Galley’s (1993) genetic model, the zone of K enrichment at Panorama is also a zone of Si en-richment rather than depletion (fig. 5.7 a, b). Brauhart et al. (2001) explained that, perhaps this corre-sponds to deeper convection early in the life of the hydrothermal system, such that Si was leached from deeper in the stratigraphy and precipitated in the zone of K enrichment and at the sea floor. The interpolated silicification map was classified into least altered, moderate and strong altered by silicification using density slicing with group domain (fig. 5.8). The classified silicification map is a useful addition to alteration mapping in identifying regional vectors to ore at Panorama.

5.3. Ground emissivity spectra

Rock samples were collected from the field and measured in a laboratory for TIR spectra in the spec-tral range of 1.6419 - 519.7599 µm wavelength. From here, the interest region of the study is 8-14 µm. 44 samples were analysed and that samples distribute mainly in the volcanic pile (fig. 5.9. a). Unfortunately, these spectra don’t have rock descriptions. In this case, rock names were derived from the geology map when overlying sample points on the geology map. Four of all samples locate on cherts. For example, the sample number PS06, which is on the marker chert, has 3 measurements (fig. 5.9 b). These emissivity spectra (Fig. 5.9 and 5.10) are all similar with quartz true emissivity (fig. 1.1) in spectral shapes except one measurement in PS06. These measurements represent the relationship be-tween amount of quartz in samples and emissivity minimum.

Figure 5.8. The classified silicification map: weak or least altered, moderate and strong al-tered; VMS deposits and classification bounds.


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a) b)

Figure 5.9 a) Sample points of the laboratory TIR spectra on the simplified geology; b) Laboratory emissivity spectra of the sample PS06 on marker chert, which has 3 (I, II, III) measurements;

a)

b)

Figure 5.10. Laboratory emissiv-ity spectra of the samples (a) PS11 and (b) PS10 on marker cherts, which have each 3 (I, II, III) measurements;


39

a) b) Figure 5.11. a) Laboratory emissivity spectra of rocks: PK03BI – rhyolite, PS004I – dacite, PK009II – andesite-basalt, PS020AII – microdiorite; b) The north part of the silicification map is overlaid by sample points, which are shown in fig. 5.9a. Thirty-two of 44 sample spectra are located on the interpolated silicification map and in the north part of the volcanics. In the figure 5.11 some sample spectra are shown from various types of igneous rocks and overlaid on the north part of the silicification map. On the emissivity spectra (fig. 5.11 a) the emissivity value increases from felsic rock (rhyolite) to in-termediate-basic rocks (andesite-basalt, microdiorite) and the location of emissivity minimum shifts to longer wavelengths. The quartz reststrahlen band feature is used to explain this, where resonance vi-brations associated with silicon-oxygen bonds in silica tetrahedra cause a decrease in emissivity. In the figure 5.12 the emissivity spectra can be read converting the spectral shape of reflectance into emissivity using the Kirchoff’s law (e = 1 – r). Emissivity spectra in the point PS009 derived from samples, which were collected from the dacite sill of the Kangaroo Caves formation near to the Sulphur Spring deposit (fig. 5.13). There are 5 measure-ments, which represent content change in dacite sample. The PS009V displays the feature of quartz-phyric dacite. The PS009II and PS009III are from an average dacite, which was influenced from both of quartz and feldspar content in dacite, and the PS009I and PS009IV are from a feldspar-phyric dacite.

Figure 5.12. The reflectance spectra of different igneous rocks in the 8–14 µm wavelength range: G – granite, S- syenite, B- basalt, D- dunite (from Salis-bury and D’Aria, 1992).


40

Figure 5.13. Laboratory emis-sivity spectra on the sample point PS009 (dacite).


41

Chapter 6. Mapping silicification from Airborne TIR data

6.1. Exploratory Data analysis

An exploratory data analyses were applied on the MASTER TIR data to recognize and describe varia-tions in the radiance at sensor. Summary statistics of MASTER bands 42 to 48 are presented in the table 6.1 and figure 6.1. Bands 40, 41, 49 and 50 are not used in this study because of the low signal to noise ratio. Channels Min. 1st quantile Median Mean 3rd quantile Max. Std. Dev.

42 7.512 9.12 9.568 9.67 10.01 11.09 0.336 43 8.359 10.15 10.67 10.78 11.19 12.52 0.403 44 8.468 10.3 10.81 10.92 11.31 12.68 0.386 45 8.66 10.15 10.59 10.68 11.02 12.01 0.332 46 8.789 10.52 11.0 11.10 11.47 12.51 0.367 47 8.819 10.34 10.8 10.88 11.25 12.36 0.35 48 8.472 9.88 10.3 10.37 10.72 11.73 0.312

Table 6.1. Summary statistics for the airborne TIR radiance data at the MASTER sensor;

Side by side boxplots present summary statistics of MASTER TIR bands including minimum, first quantile, mean, third quantile and maximum (fig. 6.1). A syncline in the surface radiance at the band 45 is related to atmospheric absorptions (O3 etc.) and an emission from siliceous occurrences in the scene at 9.3 - 10.1 µm wavelength region. A blackbody, gray-body and sandstone emission are shown with atmospheric transmittance in figure 6.2.

Figure 6.1. Boxplots of MAS-TER TIR bands (channel cen-ter): band 42 (8.16µm); 43 (8.63); 44 (9.09); 45 (9.7); 46 (10.11); 47 (10.63); 48 (11.32); dashed curve – schematic black-body curve, continues curve on the average lines – the surface radiance.


42

Figure 6.2. Atmospheric transmittance, mid-infrared is compared to scaled gray-body spectra. Most of the absorption is due to water. Carbon dioxide has a strong 15-µm band, and the dotted line shows the increased absorption due to doubling CO2. Also the blackbody emission is shown at 288 K and the gray-body emission from water and sandstone scaled to fit on this transmittance scale. The water and sandstone curves were computed from reflectance data using: 1 - reflectance times a blackbody at 288 Kelvin (from Clark R.N., 1999). Histograms are created for examining the variation of dataset in TIR bands. The normal probability Q-Q (quantile-quantile) plots can help to examine the distribution more carefully. Q-Q plots of the band 42 and 44 show two-sided long tails than expected for a normal distribution (fig. 6.3). From the ex-amination, the TIR data can be subdivided into three parts; the population with low radiance and the population with high radiance are separated by the normally distributed population with medium radi-ance value. These three populations are bounded by the radiance value of 9.2 and 10.2 in the band 42, 10.2 and 11.4 in the band 43, 10.4 and 11.4 in the band 44 and 47, 10.2 and 11.2 in the band 45, 10.6 and 11.6 in the band 46, and 10 and 10.8 in the band 48. The boundary values between these popula-tions are mostly similar in all bands, and even same in bands 44 and 47. Bands 44 and 47 are also lo-cated in the same level of boxplot in the figure 6.1. This is suggested some common ground between these two bands, although it is early to make any intrinsic explanation on it.


43

Band 42 Band 43 Band 44

Band 45 Band 46 Band 47

Band 48 Band 42 Band 44 Figure 6.3. Histograms and normal probability Q-Q plots of the TIR radiance data at sensor. The correlation matrix shows correlation of 0.89 to 0.99 among bands, and band 44 has the lowest correlation 0.89 with band 48, also 0.91 with band 47. And bands 46, 47 and 48 have the highest cor-relation 0.99. Mostly neighbor bands correlate high, and far bands correlate relatively low.

Table 6.2. The correlation matrix among TIR bands;

6.1.1. Comparison of the MASTER TIR data with the laboratory spectra

Spectral emissivities were directly measured in the laboratory using a µFTIR instrument on 44 rock samples collected from the Panorama area. The thermal spectra of these measurements were com-pared with the airborne MASTER TIR data on spectral shape. Before comparison the laboratory emis-sivity spectra was resampled using channel center wavelengths and full width half maximum (FWHM) of the MASTER TIR data. The MASTER TIR radiance data at sensor does not match in


44

shape with laboratory spectra (fig. 6.4) but it contains some information in rock content differences. The big difference between the laboratory emissivity and image TIR radiance spectra in shape is caused by several reasons. Firstly, the image data is original radiance data at sensor without atmos-pheric correction and separation of emissivity and temperature information. Secondly, the laboratory data is presented in emissivity value from 0 to 1. Thirdly, the large sampling discrepancy between these two spectra because the image data has 15 m spatial resolution, and laboratory spectra were ob-tained from a point on hand samples.

MASTER TIR

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

9.2

9.6

10.0

10.4

10.8

11.2

11.6

radi

ance

PS05 DacitePK09 Andesite-basaltPS11 ChertPK04 Rhyolite

a) b) Figure 6.4. Spectra in sample points: (a) MASTER TIR data at rock sample locations, and (b) the laboratory emissivity spectra in the point PK04 (rhyolite): red spectra – not resampled, white spectra - resampled by the channel center and FWHM of MASTER TIR data.

6.2. Image processing and analysis

6.2.1. Decorrelation stretching

In general, most of the variation in radiant spectral flux in thermal infrared measured by sensors is due to differences in surface temperature; very little variation results from differences in emissivity of the surface. The predominance of temperature effects leads to high band-to-band correlation in the data and results in low color saturation in multiband color composite images. The standard false-color composite (FCC) image of MASTER TIR band 47, 44 and 42 for red, green and blue respectively, has a withholding of both topography and emission (fig. 6.5 a) of the study area and a weak pink color for felsic and quartz-hosted units. The comparing it with the digital elevation model (fig. 6.5 b) of the area with the 10 m pixel size, the topography is described relatively weak in the FCC image of the pixel size 15 m. A decorrelation-stretched image of the Panorama district is shown in figure 6.5a in which MASTER bands 47, 44, and 42 are displayed in red, green, and blue, respectively. Areas that appear in red in figure 6.5a correspond to felsic igneous rocks, quartz-hosted sediments and silicified rocks; and areas in green correspond to basic volcanic rocks. The Strelley granite body ap-pears clearly as red areas. Strong altered (silicified) andesite-basalts of the Sulphur Spring group are occurring in a mixed display of red, green and blue colors. But none-altered or background altered


45

andesite basalts are displayed in green. Also silicified chert layers (marker horizon) and Quaternary alluvial sediments are displayed sharply in red color (fig. 6.6 a). The next procedure applied on the decorrelation-stretched image is the hue wiping, which wipes an emissivity minima corresponding to felsic rocks, quartz sandy sediments, silicified rock units and chert layers (fig. 6.6 b). It confirms the empirical observation of Sabine et al. (1994) that hue is gov-erned by the position of emissivity minima of rocks. This method is a useful for mapping silicifica-tion. The standard false-color composite image (fig. 6.5) and the decorrelation-stretched image (fig. 6.6) have similar hues, but saturations and contrasts in the decorrelation-stretched image are greater. The standard false-color composite image and the decorrelation-stretched image demonstrate the retention of terrain and compositional information.

a) b) Figure 6.5. a) The standard false-color composite image (pixel size 15 m) of MASTER TIR band 47, 44 and 42 for red, green and blue respectively, b) The digital elevation model of the Panorama with the pixel size 10m.


46

a) b) Figure 6.6. a) The decorrelation-stretched image from the MASTER TIR data, and VMS deposits (dark cross sign) in the Panorama district, bands 47, 44, and 42 are red, green, and blue respectively; b) Hue-wiped decorrelation-stretched image.

6.2.2. The spectral band ratio

The spectral band ratio method was applied on TIR bands. Ratios detect magnitude of differences be-tween spectral bands. From ratio images the band ratio (48/44) between band 48 and 44 describes best the silicification and geological units especially for chert layers (Fig. 6.7 a). It is explained by the lowest correlation (0.89) between bands 44 and 48 (table 6.2). The color-ratio-composite image of band ratios (47/46 – red, 45/44 – green, 44/43 – blue) was com-puted (fig. 6.7 b). In this image green and yellow corresponds to silica-rich rocks and river sediments, blue and brown to basic rocks. A thin bright green corresponds to chert layers. The color-ratio-composite image (fig. 6.7 b) has different hues but similar color saturations with the decorrelation-stretched image. In general, contrasts are lessened in the color-ratio-composite image, relative to the decorrelation-stretched image. The spectral ratio method is also applied on emissivity images estimated using the emissivity ap-proximation methods in next sections.


47

a) b) Figure 6.7. a) The band ratio (48/44) image; b) The color-ratio-composite image of band ratios (47/46 – red, 45/44 – green, 44/43 – blue);

6.3. Emissivity approximation methods

In this subchapter emissivity and temperature separation methods are used for mapping silicification including the emissivity normalization, reference channel emissivity and alpha residuals. Each method is considered starting from an emissivity and temperature separation, mapping silicification and fi-nally a validation using ground data.

6.3.1. Emissivity normalization method

The emissivity normalization method is relatively simple and straight given an assumed emissivity value. The emissivity normalization method gives emissivity images for every channel and one temperature image. The assumed emissivity maximum value is 0.96. From statistical parameters (table 6.3) and the graph (fig. 6.8) we can see the general behaviour of resulting values of the emissivity estimation. The low emissivity values are in bands 42, 43, 44 and 45, and the high emissivity values, which are close to 0.96, are in bands 47 and 48. There is the general trend that emissivity values increase following the increase of wavelength. The most of emissivity difference in spectral contrast occurs in 8.1 to 9.7 µm wavelength or from band 42 to band 45 respectively. The maximum variation is 0.185 range of emissivity in the band 44.


48

Channel Channel center, µm Min. Max. Mean Std. Dev.

42 8.1677 0.753 0.908 0.85 0.01 43 8.6324 0.789 0.96 0.92 0.01 44 9.0944 0.775 0.96 0.92 0.01 45 9.7004 0.844 0.941 0.9 0.01 46 10.116 0.912 0.96 0.92 0.03 47 10.6331 0.942 0.96 0.952 0.005 48 11.3293 0.925 0.96 0.94 0.01

Table 6.3. General statistics of emissivity values estimated by the emissivity normalization;

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

0.76

0.80

0.84

0.88

0.92

0.96

emis

sivi

ty

-Std_dev+Std_devMaxMeanMin

The emissivity image of the band 44 (fig. 6.9 b) discriminates geological units by emissivity value derived from the silica content differences including Strelley granite, volcanic pile and especially clears for chert layers and alluvial sandy sediments. The temperature image (fig. 6.9 a) presents area in temperature range 296 – 320 K (Kelvin) and warmer area in lighter tone. This image indicates a before afternoon, which has sunshine in east slopes of hills. And flowing water is cooler than alluvial sandy deposits. The temperature image also gives relief information of the study area.

Figure 6.8. The minimum, maxi-mum, mean and standard deviation line of emissivity val-ues on the wavelength region 8 – 11 µm.


49

a) b) Figure 6.9. (a) The temperature image: warmer area is in lighter tone, and (b) the emissivity image of the band 44 derived using the emissivity normalization method.

Figure 6.10. The color composite of emissivity images, the band 47, 44 and 42 for RGB re-spectively.

The mapping of silicification from emissivity im-ages is implemented in different ways including color composites of the whole study area, a com-parison between the interpolated silicification map of whole-rock geochemistry and emissivity images in the volcanic pile and chert layer interpretation. Emissivity images of bands 47, 44 and 42 are cho-sen to produce color composite image to give a better visual impression of the reality on the ground including a silicification and geological units (fig. 6.10). The color composite image repre-sents felsic rocks, silicified units and chert layers by pinkish colors, and basic/intermediate or unsili-cified rocks by green colors.


50

The silicification in the volcanic pile is represented by the emissivity image of the band 44, which were filtered by average 3x3 pixels and compared to the silicification map of the SiO2/Al2O3 ratio (fig. 6.11). The volcanic pile was subdivided into three separate parts, which were transformed by two ma-jor strike-slip faults and is called the north, middle and south part. The emissivity image of band 44 represents better than others the general review of silicification as the north part is weak altered, the middle part is generally moderate, and the south part is generally strong altered like in the silicifica-tion map of geochemistry. Also the increasing content of silica in the top of volcanic pile, which is one of main features in the Panorama, is sporadically detected. The interpolated silicification map of geochemistry presents the intrinsic distribution of silicification, but the emissivity image detects the surface effects, which may be influenced from surface movement of loose sediments.

a) b)

8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

0.850

0.875

0.900

0.925

0.950

emis

sivi

ty

PS34 AlliviumPK09 Andesite-basaltPK03 RhyolitePS22 GranophyrePS11 Chert

The emissivity in the band 42 is not a true emissivity minimum, is influenced from atmospheric ef-fects. Because quartz true emissivity minimum locates around of 9 µm wavelength (see fig. 1.1). Thus the closest MASTER channel to the quartz true emissivity minimum is the band 44.

Figure 6.12. Emissivity spectra of some representative points from emissivity images derived using the emissivity normalization. The spectral contrast in emissivity of different rock types also occurs in 8.6 to 9.7 µm wavelength region, and the highest contrast is in the band 44.

Figure 6.11. Visual compari-son between (a) the silicifica-tion map from SiO2/Al2O3 ra-tio, the emissivity image of the band 44 (b), which was fil-tered by average 3x3 pixels, and black points are VMS de-posits, legend colors represent the increase of silicification gradually from red to blue.


51

Chert layers are easily interpreted from emissivity images, especially in band 44, which is better than remaining bands (fig. 6.13 a). Chert layers correspond to relatively low emissivity value. The chert layer map (fig. 6.13 b) was extracted from geological maps of Van Kranendonk (2000) and Brauhart et al. (1998) for comparison with emissivity image. The identification of chert layers from the emis-sivity image matches very well with the geology map, and even some cases the emissivity image de-scribes more detail silica-rich layers than current geology map. It shows that the TIR data can be used to better map siliceous units.

a)

b)

Figure 6.13. a) The emissivity image of the band 44 in the north part of Panorama, where white tone with low emissivity corresponds to chert layer; b) Chert layers from geology maps of Van Kranendonk (2000) and Brauhart et al. (1998), where Sulphur Springs (ss) deposit for the map reference.


52

6.3.2. Reference channel emissivity

This approach is similar to the emissivity normalization method. Results of the reference channel emissivity method are a temperature image and emissivity images except for the channel 48, which is the reference channel with a constant emissivity value 0.949. Emissivity values range from 0.748 until 0.986. The general statistics of the reference channel emissivity is shown in table 6.4. Emissivity val-ues have also the general trend to increase with longer wavelength (fig. 6.14). As like the emissivity normalization, the band 44 has much variation (0.20) in emissivity between the maximum and mini-mum. The temperature image (fig. 6.15 a) of this method is similar to the one derived using the emis-sivity normalization method.

Channels Minimum Mean Maximum Std. dev. 42 0.748 0.85 0.907 0.01 43 0.780 0.92 0.966 0.01 44 0.770 0.92 0.970 0.01 45 0.836 0.90 0.940 0.01 46 0.904 0.95 0.981 0.01 47 0.931 0.96 0.986 0.00 48 0.949 0.949 0.949 0.00

Table 6.4. General statistics of the reference channel emissivity.

8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

0.76

0.80

0.84

0.88

0.92

0.96

emis

sivi

ty

MaxMeanMin

Figure 6.14. The minimum, mean and maximum spectral line of the reference channel emissivity;


53

a) b) Figure 6.15. (a) The temperature image, legend in K and (b) the emissivity image of the band 44 de-rived using the reference channel emissivity method.

Figure 6.16. The color composite im-age where band 47, 44 and 42 are for RGB respectively;

The mapping of silicification was done by the same approaches as used in the emissivity normalization. The color composite image displays silica-rich or silicified rocks in red and pink colors, and basic or unsilicified rocks in green color (fig. 6.16). The silicification in the volcanic pile is represented by emissivity image of the band 44 (fig. 6.17). As seen before, in this image also the feature of a de-creasing emissivity following an increase of silicifi-cation is kept. The part in volcanic pile was sepa-rated from the general image extend and filtered us-ing average 3x3 pixels. Marker chert layers are clearly defined on the emis-sivity image of the band 44 derived using the refer-ence channel emissivity method, in the Panorama area (Fig. 6.18).


54

a b)

Figure 6.17. a) The silicification map of geochemistry (SiO2/Al2O3 ratio) in the volcanic pile; b) the emissivity image of the band 44, which was filtered by average 3x3 pixels;

Figure 6.18. The chert layer interpretation (white lines) on the band 44 of the reference channel emis-sivity image in the north part of Panorama, Sulphur Springs (ss) VMS deposit point for the map refer-ence;


55

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

0.850

0.875

0.900

0.925

0.950

0.975

emis

sivi

ty

PS34 AlluviumPK09 Andesite-basaltPK03 RhyolitePS22 GranophyrePS11 Chert

6.3.3. Alpha residuals method

The alpha residuals method was explained in the previous chapter, thus here only results are pre-sented. The alpha residuals method produces images with alpha values for every channel instead of actual emissivity value and does not provide a temperature image. General statistics of the alpha values are presented including minimum, maximum, mean and standard deviation spectral profiles and the histogram (table 6.5, fig. 6.20). Alpha values range from -1.032 to 1.051. Most of the alpha values are around zero, and positive values are more than negative values (fig. 6.20 b), correspond to emissivity high values. The general increasing trend of alpha value is ob-served from a shorter wavelength to a longer wavelength. It describes that a low emission is in a shorter wavelength of this region. (fig. 6.20 a). Also the widest variation occurs in the band 44 (1.155). A sharp decreasing of variation between maximum and minimum occurs in the band 45 in that general up trend, and a variation increases from band 46 until band 48, which has the variation of 1.043. This shows that the alpha residuals method estimates better images in bands 47 and 48 than previous methods.

Table 6.5. General statistics of alpha residual images

Band Min Max Mean Std. Dev. 42 -1.032 -0.26 -0.65 0.07 43 -0.728 0.226 0.0 0.07 44 -0.944 0.211 -0.02 0.08 45 -0.538 0.061 -0.23 0.05 46 0.039 0.688 0.26 0.05 47 0.116 1.051 0.38 0.07 48 0.004 1.047 0.26 0.08

Figure 6.19. Emissivity spectra at selected representative sample locations from emissivity images derived using the reference channel emissivity method. The reference channel emissivity spectrum is similar to the one of emissivity normalization.


56

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

-0.7

-0.3

0.1

0.5

0.9

1.3al

pha

resi

dual

s-Std_dev+Std_devMaxMeanMin

a)

b) The alpha spectrum is extracted from some sample locations of different lithology (fig. 6.21). The main contrast between different lithology is observed in bands 44, 45 and 48. In case of chert there is very little difference from band the 43 until 45. The spectrum influenced from atmospheric effect is different from the true ground emissivity curve.

8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

alph

a va

lue

PS22 Strelley granitePK09 Andesite-basaltPS11 Chert

Figure 6.20. Alpha values of the alpha residuals method: a) Spectral profiles for minimum and maxi-mum, mean and standard deviation of alpha values, b) The histogram: Bands 42 to 48.

Figure 6.21. Alpha resid-ual spectra from the MASTER data at selected sample locations;


57

a) b) Figure 6.22. a) The alpha residual image of band 44, legends of alpha values, b) The color composite image of bands 47, 44 and 42 for red, green and blue respectively. The alpha residual images of the band 43 and 44 display a ground composition visually better than remaining bands. Here only the band 44 is presented (fig. 6.22 a). The mapping of silicification from alpha residual images contains the color composite image of bands 47, 44, and 42 for red, green and blue respectively, the silicification map in the volcanics and chert layer interpretation maps. The color composite image displays clear differences of rock compositions and alterations especially for silicification. It also clearly indicates differences between weak silicified (green color - north part of the volcanic pile and left side of the north-east main transform fault) and strong silicified (blue to pink- middle and south part of the volcanic pile) volcanic rocks mostly in the andesite-basalt unit (fig. 6.22 b). The comparison of alpha residual images with the interpolated silicification map of geochemistry in the volcanic pile shows the resulting map in the band 44 (fig. 6.23). Silicification in the volcanics was represented in a color scale where blue, green and red colors for strong, moderate and weak silicifications respectively. The alpha residuals image of the band 44 indi-cates silicification zones, which are related to the VMS deposits.


58

a) b) Marker chert layers are interpreted clearest on the alpha residuals image of band 44. In some cases, chert layers are described wider than an original size. It can be explained by several reasons of strong reststrahlen band and loose sediments around chert layers, including quartz gravels and sands or spa-tial resolution (15 m) of image and stretching of the gray scale image (fig. 6.24).

Figure 6.23. Visual compari-son of alpha residuals image of the band 44 (a), and the silicification map (b) of the SiO2/Al2O3 ratio, legend colors represent the increase of silicification gradually from red to blue, the alpha residuals image were filtered by average 3x3 pixels.

Figure 6.24. The chert layers (white lines) on the band 44 of the alpha residuals image in the north part of Pano-rama, Sulphur Springs (ss) VMS deposit point for the map reference;


59

Chapter 7. Comparison and validation

7.1. Introduction

Various methods were used for the mapping of silicification in the Panorama, including (1) the decor-relation stretching, (2) spectral ratio, (3) emissivity normalization, (4) reference channel emissivity and (5) alpha residuals method. These methods were compared and validated in sense of visual enhancement, spectral contrast and shape and correlation with geochemical data.

7.2. Comparison in visual enhancement

In order to compare the visual enhancement of different processing methods, a set of images was cre-ated using the emissivity information extracted from TIR channels 47, 44, and 42, with each technique and displayed in red, green, and blue respectively, in the north of Panorama. These images were used to produce figure 7.1, which consists of 6 panels: a) geology map, b) decorrelation stretching, c) spec-tral ratio, d) emissivity normalization, e) reference channel emissivity, f) alpha residuals method. The spectral ratio data is represented by the color-ratio-composite image of band ratios (47/46 – red, 45/44 – green, 44/43 – blue). Of the five images, the color-ratio composite image of the reference channel emissivity is the noisiest. This can be attributed to noise in the channel assumed to have a constant emissivity, being introduced into the other channels by this technique. By comparison, other images appear less noisy, and noise from one channel tends not to be introduced into the other channels. Images produced using any of the five techniques permit good discrimination of the mapped geologi-cal or siliceous units. By comparison, both the emissivity normalization and reference channel emis-sivity image appear similar. And both the decorrelation stretching and alpha residuals have excellent discrimination and appear similar color variation. This includes visual discrimination of the Kangaroo Caves Formation (AScbi), Strelley Granite (Agste), Corboy formation (AGct), Paddy Market forma-tion (AGph), Honeyeater Basalt (AGh) and Lalla Rookh Sandstone (ADlc). The andesite-basalt of the Kangaroo Caves formation can be separated into silicified (right side of fault) and unsilicified parts (left side of fault). The Strelley Granite can be easily discriminated, and chert layers have a diagnostic emission minimum in all images. A number of layered ultramafic-mafic sills (pinkish colors in the geology map) lied within the ferruginous shale and mudstone with local siltstone and sandstone (AGph) of the Paddy Market formation show effects of emission from different interlayered rock compositions. And both the turbidite sandstone (AGct) of the Corboy formation and conglomerate of the Lalla Rookh Sandstone (ADlc) are also distinctively separated from surrounding rock units due to emission from chert dominated siliceous clasts and matrixes.


60

a) b)

c) d)

e) f) Figure 7.1. Color composite images: a) geology map, b) decorrelation stretching, c) spectral ratio,


61

d) emissivity normalization, e) reference channel emissivity, f) alpha residuals method; Geological units: AScbi – andesite-basalt, ADlc – conglomerate and sandstone, AGh – basalt and dolerite, AGph - ferruginous shale and mudstone, AGct – turbidite sandstone, and Agste – Strelley granite and ss – the Sulphur Springs VMS deposit.

7.3. Correlation with whole-rock geochemistry

The mapping silicification in the volcanic pile is validated using the correlation between the SiO2/Al2O3 ratio value of 21 sample points from the whole-rock geochemistry as the ground truth and emissivity bands and band ratios (fig. 7.2) in the north part of Panorama.

The band correlations are compared to each other. In general, there is no band correlation more than 0.5 in the emissivity images. Within these correlations, the highest one (0.435) occurs in the band 45 of the emissivity normalization from emissivity images. Bands 43 and 45 have a relative correlation in the alpha residual images (Table 7.1). The original TIR radiance data at sensor has negative more cor-relation than emissivity images in all bands from 0.478 to 0.587. As a consequence, the correlation in case of the SiO2/Al2O3 ratio value of 21 samples in the Kangaroo Caves district area was converted mostly tend to from negative to positive correlation during the emissivity and temperature separation from radiance data. Although correlations are negative, it shows that the MASTER TIR radiance data hosts originally good base information at sensor.

Band 42 43 44 45 46 47 48 EN 0.185 -0.036 0.335 0.435 0.172 0.200 0.003

RCE 0.137 -0.038 0.245 0.339 0.130 -0.028 Alpha residuals 0.0317 -0.394 0.269 0.420 -0.019 -0.298 -0.194

SiO2/Al2O3

ratio

Sensor data -0.587 -0.558 -0.478 -0.484 -0.494 -0.533 -0.536 Table 7.1. Correlation of the SiO2/Al2O3 ratio versus emissivity bands derived using emissivity ap-proximation methods in the north part of Panorama: EN – emissivity normalization, RCE – reference channel emissivity;

Figure 7.2. The band combination ratio image of (44+45+46)/(43+47) from the reference channel emissiv-ity in the volcanic pile of north part of Panorama, and ground sample points of the SiO2/Al2O3 ratio value of geochemistry; the image was fil-tered using the average of 3x3 pix-els; Kangaroo Caves (kc) VMS de-posit point are for the map reference.


62

In case of band ratio or band combination ratio (table 7.2), if only highest correlations are selected from each method, the band ratio (44+45)/47 from alpha residual images has correlation of 0.393, the band combination ratio of (44+45+46)/(43+47) from the reference channel emissivity images has correlation of 0.560, the band ratio of (44+45)/43 from emissivity normalization images has correla-tion of 0.540. The reason of these high ratio correlations is related to that bands with positive correla-tion used by numerator, and bands with negative value of correlation with the SiO2/Al2O3 ratio are by denominator. After applying a 3x3 averaging filter, the band combination ratio of (44+45+46)/(43+47) from the reference channel emissivity images gained a positive correlation of 0.607 with the SiO2/Al2O3 ratio. The correlation graph (fig. 7.3) between these two values shows two outliers of 1.478 and 1.480 in band ratio values, which were influenced from silicified andesite-basalt and sandy stream sediments. The high values of SiO2/Al2O3 ratio come from felsic volcanic rocks, including silicified dacite and rhyolite. In case of the alpha residuals images, using the band ratio method did not increase the correlation coefficient. The band combination ratio of (44+45+46)/43 (fig. 7.5) from the original TIR images has correlation of 0.720, which is the highest one within these, and the scatterplot of these correlation is presented in figure 7.4, is similar to the graph of fig. 7.3.

4 5 6 7 8SiO2/Al2O3

1.469

1.472

1.475

1.478

1.481

Ban

d ra

tio (4

4+45

+46)

/(43+

47)

Image data Band ratio Correlations Emissivity normalization (44+45)/43 0.540

Reference channel emissivity (44+45+46)/(43+47) 0.607 Alpha residuals (44+45)/47 0.393

SiO2/Al2O3 ratio

Sensor data (44+45+46)/43 0.720 Table 7.2. Correlation of the SiO2/Al2O3 ratio versus emissivity band ratios derived using emissivity images and the MASTER sensor data in the north part of Panorama.

Figure 7.3. The correlation graph between the band combination ratio of (44+45+46)/(43+47) from the reference channel emissivity images and SiO2/Al2O3 ratio;


63

4 5 6 7 8SiO2/Al2O3

2.98

3.00

3.02

3.04

3.06TI

R b

and

ratio

(44+

45+4

6)/4

3

In the study area, a total 445 samples (see fig. 5.2) were analysed for the whole-rock geochemistry. From this, the SiO2 content of all samples is correlated with emissivity bands and the original TIR ra-diance data at sensor of MASTER for evaluation (table 7.3). The highest one within these is 0.308 in the band 44 of alpha residuals, and generally the band 44 is more correlated than other bands due to the location of emission minima. Alpha residual images are more correlated than emissivity normali-zation and reference channel emissivity images in four bands, including 44, 46, 47 and 48. The refer-ence channel emissivity images are also more correlated than other two in three bands, including 42,

Figure 7.4. The scatterplot be-tween the band combination ratio of (44+45+46)/43 from the TIR radiance data at sensor and SiO2/Al2O3 ratio;

Figure 7.5. The band combina-tion ratio image of (44+45+46)/43 from the TIR radiance data, white points are VMS deposits.


64

43, and 45 due to reason of used the constant emissivity value in the longer wavelength region i.e. the band 48.

Band 42 43 44 45 46 47 48 EN -0.143 -0.296 -0.304 -0.0734 -0.0739 0.009 0.061 RCE -0.176 -0.300 -0.307 -0.110 -0.111 -0.056 Alpha -0.053 -0.284 -0.308 0.09 0.132 0.201 -0.235 Sensor data 0.011 -0.016 -0.031 0.035 0.0369 0.048 0.060 Advance EN -13 18.5 9.80 -0.815 -2.002 0.187 1.01 Advance RCE -16 18.75 9.90 -3.14 -3.008 -1.166 Advance Alpha -4.8 17.75 9.935 2.57 3.577 4.187 -3.916 Table 7.3. Correlation of the SiO2 content versus emissivity bands derived using emissivity approxi-mation methods in the Panorama: EN – emissivity normalization, RCE – reference channel emissivity, Alpha – alpha residuals, Sensor data – MASTER TIR original radiance data; Advance - for example: (Advance EN = EN / Sensor data). The advance in correlation during the emissivity and temperature separation using the emissivity ap-proximation methods is evaluated using the correlation of the radiance data at sensor. The alpha re-siduals method advances in all bands and more in four bands than other two same as the correlation. The reference channel emissivity method advances in three bands more than other two same as the correlation and it has no advance in the band 47. The highest advance (18.75 times) occurs in the band 43 of the reference channel emissivity. The emissivity normalization method has no advances in bands 47 and 48.

7.4. Spectral comparison

The overall shape of the spectral curve should be adequate for comparison of airborne data and with laboratory data. The spectrum measured with the µFTIR was obtained from a point on the hand sam-ple surface. Due to this large sampling discrepancy between the laboratory and image spectra, and since the laboratory measurements were sensitive to surface roughness, shapes of the laboratory spec-tra were compared with the image spectra, not the absolute emissivity values. Spectra derived using the emissivity normalization, reference channel emissivity and alpha residuals method were compared with the resampled laboratory spectra, which were derived using the FWHM of the MASTER TIR data. In case of the alpha residuals, the alpha residuals value is derived from the laboratory emissivity spectra, with the left-hand side of equation 14 (in the chapter 4). The formula:

j

N

jjjjj N

αελελ =− =1

ln1

ln

where: λj – wavelength of channel center in µm. Spectra from other two methods can compare directly with the resampled laboratory emissivity spec-tra. Spectral comparisons (fig. 7.6) show that image spectra have an obvious difference in bands 42, 43, and 44, and a general agreement in shape from the band 45 to 48 with the laboratory spectra. The al-pha residuals spectra from a chert (PS11), dacite (PS05) and rhyolite (PK05) are closer to the labora-


65

tory spectra than other two methods. Even in the sample point of chert (PS11), the alpha residuals spectrum crosses with the laboratory spectrum in wavelengths of 8.4 and 9.78 µm, which are closer to the channel center of bands 43 and 45 respectively. In the dacite sample point (PS05), the alpha re-siduals spectrum also crosses at the wavelength 8.4 and matches in shape following with the labora-tory spectrum from the wavelength 9.7 until 10.1 µm, which are the channel center of 45 and 46 re-spectively. In the sample point of dacite sill (PK01), all image spectra have a same shape and with the laboratory spectra.

PS11 Chert

8.0 8.5 9.0 9.5 10.0 10.5 11.00.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

emis

sivi

ty

lab. dataEmiss.normRCE

PS11 Chert

8.0 8.5 9.0 9.5 10.0 10.5 11.0

-1.8

-1.4

-1.0

-0.6

-0.2

0.2

0.6

1.0

1.4

alph

a re

sidu

als

Lab. dataAlpha residual

PS05 Dacite

8.0 8.5 9.0 9.5 10.0 10.5 11.0

0.72

0.76

0.80

0.84

0.88

0.92

0.96

emis

sivi

ty

Lab. dataEmiss.normRSE

PS05 Dacite

8.0 8.5 9.0 9.5 10.0 10.5 11.0

-1.1

-0.7

-0.3

0.1

0.5

0.9

alph

a re

sidu

als

Lab. dataalpha residual

PK05 Rhyolite

8.0 8.5 9.0 9.5 10.0 10.5 11.00.68

0.72

0.76

0.80

0.84

0.88

0.92

0.96

emis

sivi

ty

RCELab. dataEmiss.norm

PK05 Rhyolite

8.0 8.5 9.0 9.5 10.0 10.5 11.0

-1.3

-0.8

-0.3

0.2

0.7

1.2

alph

a re

sidu

als

Alpha residualLab. data


66

PK01 Dacite

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

0.850

0.875

0.900

0.925

0.950

0.975em

issi

vity

Lab. dataRCEEmiss. norm

PK01 Dacite sill

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

alph

a re

sidu

als

Alpha residualLab. data

Figure 7.6. The spectral comparison with the laboratory data: Emiss. norm – emissivity normalization, RCE – reference channel emissivity, Lab. data – laboratory spectrum;

7.5. Discussion

The comparison in visual enhancement accounts all five methods applied on the MASTER TIR data for the mapping of silicification. Generally, from all these methods, the best one is decorrelation stretching by the visual comparison. From emissivity approximation methods, the alpha residual gives a better visualization than other two methods. Within emissivity images by the correlation with the SiO2/Al2O3 ratio and SiO2 content, there is not good correlation with native bands. But only in cases of the TIR radiance data at sensor and spectral band ratio, there are some correlations more than 0.5. The band combination ratio of (44+45+46)/43 from the TIR radiance images has the highest correlation of 0.720 with the SiO2/Al2O3 ratio in the Kangaroo Caves district area. By the spectral comparison, image spectra derived using different methods are similar in spectral shape. Spectra of the reference channel emissivity and emissivity normalization are close to and even overlap to each other (fig. 7.4). The alpha residuals spectra are generally closer to the laboratory spec-tra than other two methods. Until now all analyses were made on atmospherically uncorrected image data by the reason of assess-ing to the possibility of MASTER TIR data for the mapping of silicification without atmospheric cor-rection.


67

Chapter 8. Atmospheric correction

“Errors using inadequate data are much less than those using no data at all”

Babbage, Charles (1792-1871)

8.1. Introduction

Local atmospheric data of the Panorama area and an atmospheric model are not present for this study. Therefore in this study, the reference point calibration method is used for atmospheric correction. The reference point calibration method is applied to the emissivity images. Ground thermal data of se-lected points were available in the form of laboratory emissivity spectra.

8.2. Reference point calibration method

Emissivity images estimated by the emissivity approximation methods were corrected for atmospheric effects using the reference point calibration (RPC) method. The methodology of the RPC is described in the chapter 4. For reference points one used the ground emissivity spectra of 15 samples. The location of samples is shown on the geology map (fig. 8.1), the description is on the table 8.1 and spectra of emissivity images and laboratory data from some refer-ence points are compared in the figure 8.2. Spectra of other points were displayed and explained in previous chapters 6 and 7. These spectra in the figure 8.2 are generally consistent with previous ex-planations.

Table 8.1. The description of reference points in geology and alteration: fse – feldspar-sericite-quartz, cq – chlorite-quartz, trans-se – transitional sericite-quartz.


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Figure 8.1. The reference point locations and their numbers on the geology map of Van Kranendonk (2000); the legend is for geological units, where: Dalton suite: AaDd – ultramafic-mafic dykes, AaDo – gabbronorite sill, AaDx – pyroxenite sill; ADlc – Lalla Rookh sandstone; Corboy formation: AGcc – sandstone, AGct – shaley siltstones; AGh – Honeyeater basalt; AGph – siltstone and sandstone of the Paddy market formation; Strelley granite: Agste – hornblende-biotite monzogranite, Agstey – microcrystalline granophyre, Agstp – porphyritic hornblende-biotite monzogranite; Kangaroo Caves Formation: AScbi – andesite-basalt (altered including silicification), AScc – marker chert, AScfd – dacite sills, AScfr – rhyolite domes, ASci – banded iron-formation, AScsx – polymict megabreccia; ASgd - comagmatic microdiorite, Czc – Cenozoic sediments, Qaa - alluvium.

PK06 Siltstone

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PS06 Chert

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PS36 Megabreccia

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Lab.Emiss. normRCE

Figure 8.2. Spectra of emissivity images and laboratory data from some reference points: Emiss. norm – emissivity normalization, RCE – reference channel emissivity, Lab. – laboratory spectrum; And then the offset (b) of linear relationship between emissivities of the laboratory and derived from the MASTER data, and the calibrated emissivity (E) were calculated to each band separately from the band 42 to 48 for each of three methods. Also the comparison was applied between only average emissivity values instead of actual value de-rived from the MASTER TIR data and the µFTIR laboratory spectra of reference point samples, be-cause there is large sampling discrepancy between the laboratory and image spectra as mentioned be-fore.

Channel number 42 43 44 45 46 47 48

Center wavelength, µm 8.168 8.632 9.094 9.700 10.116 10.633 11.329

Emissivity MASTER 0.853 0.924 0.925 0.906 0.951 0.959 0.948

Emissivity µFTIR 0.846 0.804 0.756 0.855 0.905 0.934 0.949

% difference 0.82 12.98 18.27 5.629 4.83 2.6 0.105 Table 8.2. Comparison of the average emissivity values of the MASTER emissivity derived using the emissivity normalization and the µFTIR laboratory spectra at reference points. The percent difference of the MASTER emissivity derived using the emissivity normalization from the µFTIR laboratory spectra strongly affected by an atmosphere and ground surface in the band 43,


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44, 45 and 46 is the range of 4.83 - 18.27 %, and the percent difference not strongly affected in the band 42, 47 and 48 is relatively 0.1 - 2.6% (table 8.2). The percent difference was calculated by the converting a subtracting difference between these two values into percentage. For example: in the band 42, 0.853 - 0.846 = 0.007*100/0.853=0.82. The percentage difference in the MASTER emissivity derived using the reference channel emissivity from the µFTIR laboratory spectra strongly affected by an atmosphere and ground surface in the band 43, 44, 45 and 46 is the range of 4.93 - 18.27 %, and the percent difference not strongly affected in the band 42 and 47 is relatively 0.82 - 2.708 % (table 8.3).

Channel number 42 43 44 45 46 47 48

Center wavelength, µm 8.168 8.632 9.094 9.700 10.116 10.633 11.329

Emissivity MASTER 0.853 0.924 0.925 0.907 0.952 0.960 0.949

Emissivity µFTIR 0.846 0.804 0.756 0.855 0.905 0.934 0.949

% difference 0.82 12.98 18.27 5.73 4.936 2.708 0 Table 8.3. Comparison of the average emissivity derived from the MASTER TIR data using the refer-ence channel emissivity method and the µFTIR laboratory spectra at reference points. Percent differences of the MASTER data and laboratory data are very similar to each other or even exactly same in bands 42, 43 and 44 in cases of the emissivity normalization and reference channel emissivity methods (table 8.2 and 8.3). In case of the alpha residuals method a direct comparison of values is impossible between the alpha residuals value derived from the MASTER data and the laboratory emissivity. For the spectral comparison in chapter 7 alpha residual values were derived from the laboratory emis-sivity value (measurement - emissivity - alpha residuals). And the alpha residuals of the MASTER TIR data was derived from the radiance data at sensor (measurement – alpha residuals). Therefore the level difference of derivation sources precludes a direct quantitative comparison between alpha re-siduals. The b value was calculated, which is expressed for an atmospheric transmittance and effects from ad-ditional surface materials other than rock surfaces. Graphs of the b value (fig. 8.3) in case of the emis-sivity normalization and reference channel emissivity method are same each other and only a small difference occurs in the band 46. The shape of graphs is consistent with the MODTRAN atmospheric transmittance model in 8-11 µm wavelength region (see fig. 6.2). An atmospheric transmittance is lowest in the wavelength region of the band 44 and atmospheric windows are observed in the band 42; 45; 47 and 48.


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8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

-0.7

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0.0b

valu

eEmiss. normRCE

General statistics of emissivity values with and without atmospheric correction are compared to each other in order to see differences between two emissivity values. This comparison shows also a similar result as like the comparison with laboratory spectra and an atmospheric transmittance expressed by b value. The percent difference is less in the reference channel emissivity than the emissivity normaliza-tion. The highest percent difference between means occurs in the band 44 (31.68 % and 17.39 %), and then the band 43 has 20.57% and 11.95 % differences in two methods respectively (table 8.4, 8.5 and fig. 8.4).

Band Minimum Maximum Mean % difference Std. dev. 1 2 1 2 1 2 1 2

42 0.753 0.759 0.908 0.91 0.83 0.835 -0.602 0.043 0.042

43 0.789 0.485 0.96 0.902 0.875 0.695 20.57 0.05 0.117

44 0.775 0.306 0.96 0.877 0.868 0.593 31.68 0.054 0.165

45 0.844 0.761 0.941 0.910 0.892 0.834 6.502 0.028 0.043

46 0.912 0.833 0.96 0.924 0.936 0.879 6.089 0.014 0.027

47 0.942 0.906 0.96 0.935 0.952 0.922 3.15 0.005 0.008

48 0.925 0.922 0.96 0.959 0.943 0.941 0.212 0.01 0.011

Table 8.4. Comparison of general statistics of emissivity images with (2) and without (1) atmospheric correction derived using the emissivity normalization method.

Band Minimum Maximum Mean % difference Std. dev. 1 2 1 2 1 2 1 2

42 0.748 0.731 0.907 0.901 0.85 0.84 1.17 0.01 0.01 43 0.780 0.463 0.966 0.917 0.92 0.81 11.95 0.01 0.03 44 0.770 0.288 0.970 0.907 0.92 0.76 17.39 0.01 0.05 45 0.836 0.748 0.940 0.908 0.9 0.85 5.55 0.01 0.01 46 0.904 0.830 0.981 0.966 0.95 0.91 4.21 0.01 0.01 47 0.931 0.888 0.986 0.977 0.96 0.94 2.08 0.00 0.01 48 0.949 0.946 0.949 0.946 0.949 0.946 0.31 0.00 0.00

Table 8.5. Comparison of general statistics of the reference channel emissivity images with (2) and without (1) atmospheric correction;

Figure 8.3. Atmospheric transmittance expressed by “b” value: Emiss. Norm - Emissivity normalization, RCE - reference channel emissivity.


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8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

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The emissivity spectra derived from the atmospherically corrected emissivity images considerable differ from spectra of uncorrected emissivity images (fig. 8.5 and 8.6). After removing of atmospheric and surface effects the spectral shape is fixed, the spectral contrast between rocks is increased and emissivity value is lowered generally. Spectral profiles present some representative reference points of rock samples, name of which were derived from the geology map (fig. 8.1). The emissivity difference in the spectral contrast of the atmospherically corrected spectra between different rock types is 0.093 and 0.127 in the emissivity normalization method and reference channel emissivity respectively. It is the highest in band 44 and 3 times more than uncorrected one. This is a suitable contrast for the mapping of silicification. The emissivity minimum of spectra locates in band 44 and displays clearly the quartz reststrahlen feature by the starting up from a chert to andesite-basalt following the decrease of silica content in rocks. The emissivity (0.716 and 0.687) is derived from a chert (PS11) due to high silica content and additionally could be microcrystalline grain-size effect. And the maximum (0.809 and 0.814) in band 44 is from the point PS34, which locates in the alluvial sediment (Qaa) according to the geology map. The question is here that the name of rock sample is unknown. Spectra of a microcrystalline granophyre and a rhyolite have a similar emissivity minimum 0.768 and 0.771 respectively in the band 44 of the reference channel emissivity but they differ in the case of emissivity normalization. These two rocks have a similar content and microcrystalline grain-size. The laboratory spectra of those samples are plotted in figure 8.7. In the laboratory spectra these samples are differentiated from each other by emission minima, and the rhyolite sample has less emissivity than granophyre sample. Therefore, the reason of difference of image spectra from the laboratory spectra could be related to the general behaviour of rock surfaces within the 15 m sampling scene of imagery.

Figure 8.4. Percent differences between means of emissivity images before and after atmos-pheric correction (see table 8.4 and 8.5), RCE – reference chan-nel emissivity, Emiss. norm – emissivity normalization.


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8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

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emis

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PS34 AlluviumPK09 Andesite-basaltPK03 RhyolitePS22 GranophyrePS11 ChertPS34PK09PK03PS22PS11

Figure 8.5. Atmospherically corrected (continual line) and uncorrected (dashed line) emissivity spec-tra of representative rock samples derived from emissivity images estimated by the emissivity nor-malization method.

Reference channel emissivity

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emis

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ty PS34 AlluviumPK09 Andesite-basaltPK03 RhyolitePS22 GranophyrePS11 ChertPS34PK09PK03PS22PS11

Figure 8.6. Atmospherically corrected (continual line) and uncorrected (dashed line) emissivity spec-tra of sample points derived from emissivity images estimated by the reference channel emissivity method.


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Laboratory spectra

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PS22 GranophyrePK03 Rhyolite

Atmospherically corrected alpha residuals spectra also have a minimum in the band 44, are close to-wards and even overlap with the atmospherically uncorrected spectra from the band 45 until 47 in spectral shape and cross in the band 43 (fig. 8.8). It says that the alpha residuals method estimates well on atmospherically uncorrected image data. The spectral contrast between rocks in the band 44 reaches 0.471, which is almost four times more than one (0.09 & 0.12) in previous two methods.

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

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PS22 GranophyrePS34PK03PK09PS11PS34 AlluviumPK03 RhyolitePK09 Andesite-basaltPS11 chert

The resampled laboratory spectra and atmospherically corrected image spectra are compared in figure 8.9. Generally, the shape of image spectra agrees with the laboratory spectra. Both the emissivity nor-malization and reference channel emissivity image spectra from the point of andesite-basalt (PK09) tend to show one more emission minima located around 8 µm except major one in the channel 44. It is typical of a strong silicification and result from Si-O stretching. All spectra from rocks are dominated by quartz reststrahlen band, and modified by feldspar bands seen as a shoulder around 10 µm. The alpha residuals spectra are closer to the laboratory spectra and have stronger feldspar bands modifica-tion than other two.

Figure 8.8. Atmospheri-cally corrected (continues line) and uncorrected (dashed line) emissivity spectra of sample points derived from images esti-mated by the alpha residu-als method.

Figure 8.7. The laboratory emissivity spectra at sample points PK03 and PS22.


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PS11 Chert

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PS05 Dacite

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Emiss. normLab. dataRCE

PK04 Rhyolite

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PS11 chert

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PS05 Dacite

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1

2

3

4

Figure 8.9. Comparison of emissivity spectra derived using the emissivity normalization (EN), refer-ence channel emissivity (RCE), alpha residuals and laboratory data (Lab. data) on sample points.

8.3. Validation after atmospheric correction

This validation was applied on the mapping silicification in the volcanic pile using the SiO2/Al2O3 ratio value of 21 sample points from the whole-rock geochemistry (see fig. 7.2). The spectral shape, contrast and visual enhancement are discussed above. Correlations with the SiO2/Al2O3 ratio of at-mospherically corrected and uncorrected emissivity bands are similar each other and the corrected one is less than uncorrected one in bands 42 and 44 (Table 8.6 and 8.7).


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Band 42 43 44 45 46 47 48 EN 0.182 -0.041 0.334 0.430 0.173 0.200 0.003

RCE 0.135 -0.038 0.245 0.334 0.131 -0.028 Alpha residual 0.031 -0.396 0.268 0.422 -0.020 -0.294 -0.194

Table 8.6. Correlation of the SiO2/Al2O3 ratio versus atmospherically corrected emissivity bands de-rived using emissivity approximation methods in the north part of Panorama: EN – emissivity nor-malization, RCE – reference channel emissivity,

Band 42 43 44 45 46 47 48 EN 0.185 -0.036 0.335 0.435 0.172 0.200 0.003

RCE 0.137 -0.038 0.245 0.339 0.130 -0.028 Alpha residuals 0.0317 -0.394 0.269 0.420 -0.019 -0.298 -0.194 Table 8.7. Correlation of the SiO2/Al2O3 ratio versus atmospherically uncorrected emissivity bands derived using emissivity approximation methods in the north part of Panorama: EN – emissivity nor-malization, RCE – reference channel emissivity; The band combination ratio of (44+45+46)/(43+47) from the reference channel emissivity images had the high correlation of 0.607 with the SiO2/Al2O3 ratio before atmospheric correction, but after atmospherically corrected the correlation was decreased and became 0.556. Also the correlation of band ratio (44+45)/43 from the emissivity normalization was decreased from 0.540 to 0.515. But the band ratio of 43/45 (fig. 8.11) from the atmospherically corrected alpha residual images has the correlation of 0.776 with the SiO2/Al2O3 ratio. This correlation (0.776) is the highest one at all (fig. 8.10). From the atmospherically uncorrected alpha residuals images, the ratio of bands 43/45 has no good correlation (0.367). Thus it is an improvement for the mapping of silicification resulted by the combined work using the alpha residuals method, reference point calibration and spectral band ratio approach.

Image data Band ratio Correlations Emissivity normalization (44+45)/43 0.515

Reference channel emissivity (44+45+46)/(43+47) 0.556

SiO2/Al2O3 ratio

Alpha residuals 43/45 0.776 Table 8.8. Correlation of the SiO2/Al2O3 ratio versus emissivity band ratios derived using atmospheri-cally corrected emissivity images.


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4 5 6 7 8Si2O/Al2O3

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2.0

2.5

3.0

3.5

Ban

d ra

tio 4

3/45

Figure 8.10. The scatterplot between the band ratio of 43/45 of the atmospherically corrected alpha residuals image and SiO2/Al2O3 ratio;

Figure 8.11. The band ratio im-age of 43/45 derived using the atmospherically corrected alpha residuals images; white points are VMS deposits.


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8.4. Discussion

Main result from the atmospheric correction using the reference point calibration method is an emis-sivity minimum determined in the MASTER sensor precision of wavelength by the fixing of spectral shape. During the correction an atmospheric transmittance was expressed. The atmospherically uncor-rected alpha residuals spectra are more close to the corrected one than other two methods. The visually enhanced images are almost same before and after of the atmospheric correction and im-ages are highly correlated (correlation = 1) each other due to linear relationship. The percent differences between atmospherically uncorrected image emissivity and the laboratory emissivity are very similar or even exactly same in bands 42, 43 and 44 in the emissivity normaliza-tion and reference channel emissivity methods (table 8.2 and 8.3). But percent differences between means of emissivity images before and after atmospheric correction are different in value and same in shape in two methods. This percent difference in the reference channel emissivity is less than the emissivity normalization. It may be expressed proportional to that estimated emissivity is how close to corrected one. After the atmospheric correction, correlations between the SiO2/Al2O3 ratio and emissivity images or image ratios are not increased definitely except the band ratio 43/45 of the alpha residuals images.


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Chapter 9. Conclusions

9.1. Conclusions

9.1.1. Silicification from ground data

• Siliceous units are well described by geological map and descriptions, but alteration of silici-fication is best determined by geochemistry. The VMS deposits are associated with seafloor alteration zones in and below silica-carbonate laminitis or chert layers, at the top of a se-quence of silicified volcanic pile at the Panorama area.

• The interpolated silicification map in the volcanic pile using the inverse distance moving av-erage method represented by the SiO2/Al2O3 ratio from the whole-rock geochemistry can indi-cate result of convective hydrothermal processes, which were keen to form VMS deposit. The interpolated silicification map coincides with previous alteration and SiO2 mass transfer maps of Brauhart et al., 1998 and 2001 respectively.

• The classified silicification map by an intensity of silicification based on the interpolated map is a useful addition to alteration mapping in identifying regional vectors to ore at the Pano-rama area.

9.1.2. Mapping silicification from airborne TIR data

The mapping of silicification from the airborne MASTER TIR data is implemented using various methods based on emittance in the TIR wavelength region, and validated using the ground data. Vari-ous methods for emissivity and temperature separation from TIR data were reviewed from literature sources. Three algorithms, including the emissivity normalization, reference channel emissivity and alpha residuals, were selected to use for the mapping of silicification, based on previous experiences and software availability. Accurate correction for atmospheric influences of the MASTER TIR data was not possible since local atmospheric data of the Panorama area and a suitable atmospheric model were not available. This problem was solved by selection of the reference point calibration method for atmospheric correction of the TIR data. The reference point calibration method uses ground thermal data, which was avail-able in the form of emissivity spectra. Additionally and fortunately the MASTER TIR data was not strongly affected by the atmosphere and is less than 0.5 %, as established by previous work (Hook et al., 2001) in a test area.

• The uncorrected and unprocessed radiance at sensor data of the MASTER instrument contains useful information of ground surface emissivity and temperature. This information can easily be visualized or enhanced by false color composite images. Some basic interpretations about silica rich units are possible in these images. The MASTER TIR radiance at sensor data has a good negative correlation (-0.587) with the SiO2/Al2O3 ratio.

• A more advanced method than using simple color composites is the decorrelation stretching. The result of this approach is useful for an enhancement of silica units and variation of silici-fication. Hue wiping applied on the decorrelation stretched image. This is a suitable method to extract only siliceous units from an exploration or study area.


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• Another fast and powerful approach for mapping variation in silica content is the spectral band ratio method. Various channels or channel combinations of radiance data or emissivity data can be rationed with or without atmospheric correction. The ratio method can be applied in various stages of the study. The band combination ratio of (44+45+46)/43 from the unproc-essed radiance at sensor TIR images has a correlation of 0.720 with the SiO2/Al2O3 ratio of the 21 samples. From this it can be concluded that the spectral band ratio method is strictly speaking the best method for the mapping of silicification and chert layers without going into temperature and emissivity separation procedures or atmospheric correction.

• The emissivity normalization is an emissivity approximation method to get an idea about the actual emissivity value, although it uses an assumed emissivity maximum (0.96). The result-ing emissivity spectra, which were subsequently corrected for atmospheric are most similar to the laboratory spectra of ground samples. The emissivity images derived using the emissivity normalization method show a better correlation with the SiO2/Al2O3 ratio than images derived by other emissivity estimation methods.

• The reference channel emissivity method is a similar method compared to the emissivity nor-malization. It has the disadvantage that it assumes a constant emissivity in one of the chan-nels. That reference channel is not available for further emissivity analysis. But the assumed constant emissivity is a realistic average of laboratory spectra of ground samples. Within the atmospherically uncorrected emissivity images, the band combination ratio (44+45+46)/43+47) derived using the reference channel emissivity method gives a high corre-lation of 0.607 with the SiO2/Al2O3 ratio.

• The alpha residuals method is the best method of the three for emissivity approximation based on comparison of images with the geology and silicification maps. However the emissivity values are not expressed as actual emissivity but as alpha residuals. The alpha residual spec-trum preserves the shape of the emissivity spectrum.

• The main result from atmospheric correction using the reference point calibration method is ability to determine the emissivity minimum in band 44. The location of the wavelength of the emissivity minimum is used to quantify silica content.

• Results of the mapping silicification from airborne TIR data show that the ratio of (band 43/ band 45) of atmospherically corrected alpha residuals images correlates highest (correlation coefficient of 0.78) with a SiO2/Al2O3 ratio derived from whole-rock analysis. This is the re-sult of combined process of temperature emissivity separation using the alpha residuals method, atmospheric correction using the reference point calibration, and then portray varia-tions in silica content using the spectral band ratio method.

• A general conclusion is that the airborne MASTER TIR data is of sufficient quality to map silicification without atmospheric correction or complex analyses.

9.2. Recommendation

• Silicification is often a good guide for mineral deposits especially for base metal and gold etc. Siliceous exhalites including cherts and cherty-iron formations may be spatially associated with a diversity of ore deposits. Therefore remote detection and identification of silicification and siliceous rock units is important in geological mapping and exploration for mineral deposits that are associated with silicic alteration.


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• Mapping silicification from airborne TIR data can be used to more accurately geological and alteration map of the area than given on current maps.

• For further research, remote quantitative estimation of silicification from TIR scanner is needed to fit a reflectance spectrum of sensor with a suitable model such as Gaussian using a numerical algorithm as like MINMAP. The recommended literature on this issue is Sabine et al., 1994.

• Beside the aim of this research work, remote detection of silicified carbonate units of the Panorama area may be useful for a paleontological study related to the discovery of the oldest stromatolities (3.45 Ga) in world preserved in the Warrawoona Group.


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Reference Brauhart, C.W., Groves, D.I. and Morant, P., 1998. Regional Alteration Systems Associated with

Volcanogenic Massive Sulfide Mineralization at Panorama, Pilbara, Western Australia. Eco-nomic Geology. Vol. 93. pp. 292-302.

Brauhart, Carl W., 1999. Regional Alteration Systems Associated with Archean Volcanogenic Mas-sive Sulfide Deposits at Panorama, Pilbara, Western Australia. Volume 1; 2. PhD thesis. The University of Western Australia.

Brauhart, C. W., Huston, D. L. and others, 2001. Geochemical mass-transfer patterns as indicators of the architecture of a complete volcanic-hosted massive sulfide hydrothermal alteration sys-tem, Panorama district, Pilbara, Western Australia. Economic geology, Vol. 96, pp. 1263-1278.

Clark, R.N., 1999. Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. Manual of Remote Sensing, Chapter 1. U.S. Geological Survey, source: http://speclab.cr.usgs.gov

Evans, A.M., 1993. Ore geology and industrial minerals, an introduction. 3rd ed. (Geoscience texts), Blackwell Science.

Farmer, V.C. (ed.), 1974. The infrared spectra of minerals. Mineralogical Society, London. Gillespie, A. R., 1992. Enhancement of multispectral thermal infrared images: Decorrelation contrast

stretching. Remote Sens. Environ. 42: 147-155. Gillespie, A. R., Rokugawa, S., Hook, S., J., Matsunaga, T. and Kahle, A. B., 1999. Tempera-

ture/Emissivity Separation Algorithm Theoretical Basis Document, Version 2.4. NASA Con-tract NAS5-31372.

Grant, J.A., 1986. The isocon diagram-a simple solution to Gresens’ equation for metasomatic altera-tion. Econ.Geol., 81. 1976-82.

Grey, K., Hickman, A. H., Van Kranendonk, M. J., and Freeman, M. J., 2002. 3.45 billion year-old stromatolities in the Pilbara region of Western Australia: proposals for site protection and public access: Western Australia Geological Survey, Record 2002/17, 11p.

Hook, S. J., Gabell, A. R., Green, A. A., Kealy, P. S., 1992. A Comparison of techniques for Extract-ing Emissivity information from Thermal infrared data for Geologic studies. Remote Sens. Environ. 42:123-135.

Hook, S. J., Myers, J. J., Thome, K. J., Fitzgerald, M. and A. B. Kahle, 2001. The MODIS/ASTER airborne simulator (MASTER) - a new instrument for earth science studies. Remote Sensing of Environment, vol. 76, Issue 1, pp. 93-102.

Hunt, G. R. 1977. Spectral signatures of particulate minerals in visible and near infrared, Geophysics, vol. 42, pp. 501-513.

Hunt, G. R., 1980. Electromagnetic radiation: The communication link in remote sensing. In Remote Sensing Geology, edited by B. S. Siegel and A. R. Gillespie (New York: John Wiley and Sons), pp. 5–45.

Hunt, J. M., Wisherd, M. P. and Bonham L. C. 1950. Infrared absorption spectra of minerals and other inorganic compounds. Analytical Chemistry, vol. 22, pp. 1478-1497.

Hunt, G. R., and J.W. Salisbury. 1974. Mid-infrared spectral behavior of igneous rocks, NTIS Envi-ron. Res. Pap. Natl. Tech. Inf. Serv., Springfield, Va.

Ilwis 3.0 Academic. 2001. User’s guide, ITC., Enschede, The Netherlands. Karr, C., Jr. (ed.), 1975. Infrared and Raman Spectroscopy of lunar and terrestrial minerals. Aca-

demic, San Diego, California. Kealy, P.S. and Hook, S.J., 1993. Separating temperature and emissivity in thermal infrared multis-

pectral scanner data: Implications for recovering land surface temperatures. IEEE transac-tions on Geoscience and Remote Sensing, vol. 31, no. 6. pp. 1155-1164.

Kirkland, L.E., Herr, K. C., and Salisbury, J. W., 2001. Band detection limits in thermal infrared spec-tra. Applied optics, 40, 4852-4862.

Kirkland, L., Herr, K., and Keim, E., et al., 2002. First use of an airborne thermal infrared hyperspec-tral scanner for compositional mapping. Remote Sensing of Environment, 80, 447-459.


83

Launer, P.J., 1952. Regularities in the infrared absorption spectra of silicate minerals. Am. Mineral. 37, 764-784.

Lazarev, A.N., 1972. Vibrational spectra and structure of silicates. Consultants Bureau, New York. Li, Z.L., Becker, F., Stoll, M.P. and Wan, Z., 1999. Evaluation of six methods for extracting relative

emissivity spectra from thermal infrared images. Remote Sens. Environ. 69:197-214. Lyon, R. J. P., 1962. Minerals in the infrared – A critical bibliography. Stanford research institute,

Palo Alto, California. Lyon, R. J. P., 1965. Analysis of rocks by spectral infrared emission (8-25 µm). Econ. Geol., 60. 715-

736. Meyer C. & Hemley J.J., 1967. Wall rock alteration. In Barnes H.L. (ed.), Geochemistry of Hydro-

thermal Ore deposits, 166-235. Holt, Rinehart and Winston, New York. Rose A.W. & Burt D.M., 1979. Hydrothermal alteration. In Barnes H.L. (ed.), Geochemistry of

Hydrothermal Ore deposits, 2nd edn. 173-235. Willey, New York. Sabine, C., Realmuto, V.J. and Taranik, J.V., 1994. Quantitative estimation of granitoid composition

from Thermal Infrared Multispectral Scanner (TIMS) data, Desolation Wilderness, northern Sierra Nevada, California. Journal of Geophysical Research, vol. 99, pp. 4261-4271.

Salisbury, J. W. and D’Aria, D. M. 1992. Emissivity of Terrestrial Materials in the 8-14 µm Atmos-pheric Window. Remote Sens. Environ. 42:83-106.

Salisbury, J. W., L.S. Walter and N. Vergo, 1987. Mid-infrared (2.1 to 25 µm) spectra of minerals. First edition, U.S. Geol. Surv. Open File rep., 87-263.

Salisbury, J. W., L.S. Walter, and D. D’Aria. 1988. Mid-infrared (2.5 to 13.5 µm) spectra of igneous rocks. U.S. Geol. Surv. Open File Rep., 88-686.

Solomon M., Walshe J.L. & Eastoe C.J., 1987. Experiments on convection and their relevance to the genesis of massive sulphide deposits. Aust. J. Earth Sci., 34, 311-23.

Thompson, J.L. and J.W. Salisbury. 1993. The mid-infrared reflectance of mineral mixtures. Remote Sens. Environ., 45. 1-13.

Van Kranendonk, M.J., 2000. Geology of the North Shaw, 1:100 000 sheet. Geological Survey of Western Australia, Perth.

Vearncombe, E. S., 1996. Volcanogenic massive sulphide-sulphate mineralization at Strelley, Pilbara Craton, Western Australia. PhD thesis, The University of Western Australia. Volume 1; 2.

Vearncombe, S., Vearncombe, J. R., Barley, M. E., 1998. Fault and stratigraphic controls on volcano-genic massive sulphide deposits in the Strelley Belt, Pilbara Craton, western Australia. Pre-cambrian Research, 88, 67-82.

Vincent, R. K. and Thomson, F. 1972. Spectral composition imaging of silicate rocks. Journal of Geophysical Research, 77, 2465-2472.

Watson, K., 1992. Spectral Ratio Method for measuring Emissivity. Remote Sens. Environ. 42:113-116.


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Appendix:

Figure 3.4. Alteration map and VMS deposits in the research area, Panorama, Australia (Brauhart et al., 1998).

a) Figure 5.6. a) Silicification in volcanics represented by SiO2/Al2O3 ratio, interpolated by the inverse distance moving average method and points show VMS deposits, legend represents the degree of sili-cification;


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a) Figure 5.11. a) Laboratory emissivity spectra of rocks: PK03BI – rhyolite, PS004I – dacite, PK009II – andesite-basalt, PS020AII – microdiorite;

Figure 5.13. Laboratory emis-sivity spectra on the sample point PS009 (dacite).


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a) b) Figure 6.5. a) The standard false-color composite image (pixel size 15 m) of MASTER TIR band 47, 44 and 42 for red, green and blue respectively, b) The digital elevation model of the Panorama with the pixel size 10m.


87

a) b) Figure 6.6. a) The decorrelation-stretched image from the MASTER TIR data, and VMS deposits (dark cross sign) in the Panorama district, bands 47, 44, and 42 are red, green, and blue respectively; b) Hue-wiped decorrelation-stretched image.


88

b)

a) b)

Figure 6.10. The color composite of emissivity images derived using emissivity normalization method, the band 47, 44 and 42 for RGB re-spectively.

Figure 6.7. b) The color-ratio-composite image of band ratios (47/46 – red, 45/44 – green, 44/43 – blue);

Figure 6.11. Visual compari-son between (a) the silicifica-tion map from SiO2/Al2O3 ra-tio, the emissivity image of the band 44 (b), which was fil-tered by average 3x3 pixels, and black points are VMS de-posits, legend colors represent the increase of silicification gradually from red to blue.


89

b)

Figure 6.16. The color composite im-age where band 47, 44 and 42 are for RGB respectively;

Figure 6.17. b) The emissiv-ity image of the band 44, which was filtered by aver-age 3x3 pixels;

Figure 6.20. Alpha values of the alpha residuals method: b) The histogram: Bands 42 to 48.


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8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5al

pha

valu

e

PS22 Strelley granitePK09 Andesite-basaltPS11 Chert

b) a)

Figure 6.21. Alpha resid-ual spectra from the MASTER data at selected sample locations;

Figure 6.23. Alpha residuals image of the band 44 (a); legend colors represent the increase of silicifica-tion gradually from red to blue, the alpha residuals image were filtered by average 3x3 pixels.

Figure 6.22. b) The color composite im-age of bands 47, 44 and 42 for red, green and blue respectively.


91

a) b)

c) d)

e) f) Figure 7.1. Color composite images: a) geology map, b) decorrelation stretching, c) spectral ratio, d)


92

emissivity normalization, e) reference channel emissivity, f) alpha residuals method; Geological units: AScbi – andesite-basalt, ADlc – conglomerate and sandstone, AGh – basalt and dolerite, AGph - fer-ruginous shale and mudstone, AGct – turbidite sandstone, and Agste – Strelley granite and ss – the Sulphur Springs VMS deposit.

8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

0.72

0.75

0.78

0.81

0.84

0.87

0.90

0.93

0.96

emis

sivi

ty

PS34 AlluviumPK09 Andesite-basaltPK03 RhyolitePS22 GranophyrePS11 ChertPS34PK09PK03PS22PS11

Figure 8.5. Atmospherically corrected (continual line) and uncorrected (dashed line) emissivity spec-tra of representative rock samples derived from emissivity images estimated by the emissivity nor-malization method.

Reference channel emissivity

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

0.69

0.72

0.75

0.78

0.81

0.84

0.87

0.90

0.93

0.96

emis

sivi

ty PS34 AlluviumPK09 Andesite-basaltPK03 RhyolitePS22 GranophyrePS11 ChertPS34PK09PK03PS22PS11

Figure 8.6. Atmospherically corrected (continual line) and uncorrected (dashed line) emissivity spec-tra of sample points derived from emissivity images estimated by the reference channel emissivity method.


93

8.0 8.5 9.0 9.5 10.0 10.5 11.0wavelength, um

-2.00

-1.35

-0.70

-0.05

0.60

1.25

1.90

2.55

3.20

3.85

4.50al

pha

resi

dual

PS22 GranophyrePS34PK03PK09PS11PS34 AlluviumPK03 RhyolitePK09 Andesite-basaltPS11 chert

Figure 8.8. Atmospheri-cally corrected (continues line) and uncorrected (dashed line) emissivity spectra of sample points derived from images esti-mated by the alpha residu-als method.


94

Munkhjargal TODBILEG

P.O. Box 159 Ulaanbaatar - 37 211137. Mongolia E-mail: [email protected]

Education: 1979-1987: Secondary school, Gobi-Altai, Mongolia. 1987-1990: The technical college with distinction, Ulaanbaatar, Mongolia. 1991-1995: Bachelor of Science in geology with distinction, Mongolian Technical University (MTU). 1998-1999: Master of Science (MSc) in Geology of Mineral Deposits, MTU. 2001-2003: MSc in Geo-information Science and Earth Observation, Mineral Resources Exploration and Evaluation specialisation, ITC, The Netherlands; Employment 1996-1997 – Geologist, The geological mapping project of the Mongolian Geological Survey. 1997-1999 – Field geologist, Quincunx Gold Exploration Company of Canada. 1999-2001 – Assistant lecturer, The School of Geology, MTU.

Documents

IDENTIFICATION OF SILICIFICATION USING …...man from the ITC. I thank all lecturers and staff members of ESA (Earth System Analysis) department of ITC, including Prof. Dr. Freek van