PART B TEXT

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[FIELD SCHOOL REPORT, UNIVERSITY OFIBADAN]

Chapter Six

PART B: SPECIALIZATION REPORT

DEPOSITIONAL PROCESSES AND PROVENANCE DETERMINATION USINGGRAIN SIZE ANALYSIS AND HEAVY MINERAL ASSEMBLAGES OF

SURFICIAL SEDIMENTS AND STREAM SEDIMENT SAMPLES FROM IWERE-

ILE AND ITS ENVIRONS.

6.0 General Statement

Iwere-Ile is a town in Iwajowa LGA, Oyo state, south western Nigeria and it is

located within the southwestern Nigerian Basement Complex. The basement

geology is characterized by distinct lithologies and mineralization. The area

covers an aerial extent of about 198 km2

and is surrounded by basementrocks. It is almost divided into two by the River Oyan which flows throughout

the mapped area from north to south forming the major drainage of the area.

The sediment load of the river is predominantly those derived from the

weathering of the surrounding basement rocks. As a requirement for

graduating from the department of Geology, University of Ibadan, it is

required that every student embark on a field school exercise where he/she

is exposed to the rudiments of geologic mapping. Also a specialization report

is required from each student, that incorporates his area of specialty into the

overall geology of the area mapped area and that constitutes this part of my

report.

The study area was mapped and the geologic map was produced by

petroleum group 2. Fifteen soil samples (surficial sediments) and eleven

stream sediments were collected with a good spread throughout the study

area (see figure 6.1). Not much has been done on the grain size analysis and

heavy mineral assemblages of Iwere-Ile and its environs. This part of my

report incorporates the use of grain size analysis and heavy mineral analysis

to determine the provenance (source) and the maturity index and to

categorize the mechanism and environment of deposition using statistical

parameters derived from the analysis.

Okon Emmanuel Etim: 148581 | M.Sc Petroleum Geology Group 2 56


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6.1 Grain size Analysis

This is also known as granulometric analysis is perhaps the most basic

sedimentological technique to characterize and interpret sediments and

sedimentary rocks. Sediment describes what settles at the bottom of a

liquid. Surface textures of sediments are a result of transport processes and

may reflect origin of particles or environment of deposition e.g. v-shaped

depression is common in deep sea sands deposited by turbidity current,

concoidal patterns may represent glacial origin, dull surfaces may indicate

etching by sand dunes in arid regions while particles from beaches and rivers

show shiny and polished surface. Sedimentological study commences with

the description of the physical properties of the deposit in question.

Grain size statistical parameters can be related to different environments of

deposition (Folk and Ward, 1957, Friedman 1967, Visher 1969). Such grain

size parameters are very useful in environmental interpretation especially

when they are integrated with other parameters such as sedimentary

structures and geological settings.

6.1.1 Methodology

The fifteen soil samples collected from the field were subjected to grain size

analysis after air-drying them, disaggregating and removing all the roots and

plant materials that may be included in the sample. 100g each of the samplewas weighed out using a weigh balance and the sample was emptied onto

the top sieve of a nest of sieves that ranged from very coarse sand size (-

1.0) to very fine sand size (4.0). The sieve was capped and the stack set

on an Endocotts sieve shaker for a constant time of 15 minutes. The weight

retained in each sieve and the pans were obtained by subtracting the weight

of the empty sieve/pan from that with sediments, then the weight was

recorded. The cumulative weight percentages were determined and

tabulated as shown in table 6.1.

The following precautions were taken during the sieve analysis:

The nest of sieves was held parallel to the base of the shaking

apparatus.

A constant sieving time of 15 minutes was used for all samples.

Sieve residues were checked for particle aggregation with the aid of a

hand lens.



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A toothbrush was used to dislodge grains stuck to the screen.

A sample weight of 100g was not exceeded in order to avoid

overloading and to facilitate particle movement downward.

TABLE 6.1: WEIGHT RETAINED IN THE RESPECTIVE SIEVES FOR ALL SOIL SAMPLES (IN GRAMS)

SIEVE SIZES1mm 850m 600m 425m 300m 212m 150m 75m


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Cumulative frequency curves and histograms were plotted for sample. Grain

sizes corresponding to the 5th, 16th, 25th, 50th, 75th, 84th and 95th percentiles

were obtained and used to calculate the graphic mean, median, mean,

standard deviation (sorting), inclusive graphic skewness and graphic

kurtosis. The formulae proposed Folk and Ward (1957) were used for thecalculation). The phi values for the various percentiles are given below:

Table 6.2: Phi () Values obtained from the Cumulative Curves

EQUIVALENT

SAMPLE NO

5 16 25 50 75 84 95

C001 -0.90 -0.65 -0.45 0.15 0.90 1.30 2.30C002 -0.95 -0.80 -0.68 -0.35 -0.05 0.30 1.50

C003 -0.75 -0.20 0.15 0.95 1.60 1.90 2.75C004 -0.75 -0.25 0.10 1.20 2.15 2.50 3.65C005 -0.70 -0.05 0.58 1.43 2.03 2.35 3.00C006 -0.83 -0.45 -0.15 0.90 1.60 1.90 2.35C007 0.15 1.30 1.45 1.85 2.18 2.35 2.85C008 -0.75 -0.20 0.25 1.65 2.55 3.25 4.05C009 -0.10 1.25 1.90 2.40 2.80 3.15 3.70C010 -0.75 -0.20 0.25 1.40 2.25 2.50 3.35C011 -0.80 -0.40 -0.08 0.85 1.70 2.15 2.90C012 -0.70 -0.05 0.75 2.20 2.75 3.25 4.00C013 -0.88 -0.60 -0.40 0.50 2.00 2.40 3.20C014 -0.80 -0.35 0.00 1.30 1.95 2.40 3.55C015 -0.85 -0.55 -0.30 0.95 2.00 2.40 3.45

Mean (Mz): gives the best measure for determining the overall size, it is

calculated using the formula,

It corresponds very closely to the mean as computed by the method ofmoments, yet is much easier to find. It is much superior to the medianbecause it is based on three points and gives a better overall picture. Thevalue obtained can be read-up from the chart given below.



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Figure 6.2: Chart showing the various grain sizes

Inclusive Graphic Standard Deviation (I): the standard deviation is ameasure of the sorting of the sediments and can be calculated using theformula given below,



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Measurement of sorting values for a large number of sediments has

suggested the following verbal classification for sorting for each value of

inclusive graphic standard deviation. Whatever value is obtained from the

calculation can be interpreted using the table below:

Table 6.3: Verbal description sorting

phi ()Size Range Verbal Description of Sorting

4.0 phi extremely poorly sortedInclusive Graphic Skewness (SkI): This is a measure of asymmetry. A

positive skewness denotes calmness in the depositional process and that the

sediments have a lot of fines, while negative skewness indicates that there

was turbulence and the particles are coarser. The formula is given as:

The verbal description for these values is given below in table 6.4:

Table 6.4: Verbal description for Skewness

Skewness Verbal Descriptionof Skewness

from +1.00 to +0.30 strongly fine skewedfrom +0.30 to +0.10 fine skewedfrom +0.10 to -0.10 near symmetricalfrom -0.10 to -0.30 coarse skewedfrom -0.30 to -1.00 strongly coarse

skewed

Gaussian Kurtosis (KG): this is a quantitative measure used to describe thedeparture from the normal probability curve which should follow the

Gaussian formula:



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The phi diameter interval between the 5 phi and 95 phi points should be

exactly 2.44 times the phi diameter interval between the 25 phi and 75 phi

points. If the sample curve plots as a straight line on probability paper (i.e., if

it follows the normal curve), this ratio will be obeyed and we say it has

normal kurtosis (1.00). Departure from a straight line will alter this ratio, andkurtosis is the quantitative measure used to describe this departure from

normality.

Table 6.5: Verbal description for Kurtosis

Kurtosis Value Verbal Description

of Kurtosis< 0.67 very platykurtic

0.67 - 0.90 platykurtic0.90 - 1.11 mesokurtic

1.11 - 1.50 leptokurtic1.50 - 3.00 very leptokurtic

> 3.00 extremely leptokurtic

The table below summarizes the calculated statistical parameters for thesamples.

Table 6.6: The results for Statistical parameters

SAMPLE NOMEAN

(Mz)

SORTIN

G ()

SKEWNESS

(SKI)KURTOSIS (KG)

C001 0.47 0.97 0.52 0.97

C002 -0.28 0.64 0.35 1.59

C003 0.88 1.06 -0.03 1.00C004 1.15 1.35 0.03 1.24

C005 1.24 1.16 -0.12 1.05C006 0.78 1.07 -0.12 0.74

C007 1.83 0.67 -0.15 0.30

C008 1.57 1.65 -0.04 0.86C009 2.67 1.05 -0.26 1.64

C010 1.23 1.30 -0.12 0.53

C011 0.98 1.20 0.23 0.85C012 1.80 1.54 -0.30 0.96

C013 0.77 1.37 0.42 0.70

C014 1.12 1.34 -0.07 0.91C015 0.93 1.39 0.07 0.46

From the results obtained from the sieve analysis, ogives (cumulative

curves) and histograms were generated. The statistical parameters were

generated from the ogives and used for the computations. Below is an

example of the data presented, while the others are placed in the

appendices. It should be noted that some of the ogives did not have values



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for 5th percentile to 25th; the curves here were extrapolated to the size

corresponding to -1 phi which indicated the top of the coarse sand size scale.

This is a standard practice as has been used successfully by earlier workers

(Kelly and Baker, 1966).

Figure 6.3: Ogive plot for sample C001

Figure 6.4: Histogram for sample C001

6.2 Heavy Mineral Analysis

The term heavy mineral generally applies to minor accessory mineral

constituents of rocks having ordinarily specific gravities higher than2.89g/cm3, which is the SG of bromoform (the preferable liquid used in heavy

mineral separation). The study includes fractions of grains that are

transparent (non-opaque), non-micaceous and detrital heavy fractions in

sediments or sedimentary rocks. Heavy mineral analysis have successfully

been used to solve stratigraphic problems often when fossils are absent in

sandstones and similar rock types. They have also been useful in provenance

studies and maturity determinations in sands and sandstones.

6.2.1Methodology

Although twenty-six samples were collected from the study area comprising

eleven stream sediments and fifteen soil samples; five stream sediments and

one soil sample were selected for heavy mineral analysis judging by their

distribution throughout the study area. The reason for selecting few samples

was based on the scarcity of analytical chemicals within our reach.

The selected samples were disaggregated and all roots and plant remains

were picked out. 50g was taken for washing with water and decanting all

clay very fine silt (usually


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with distilled water to neutralize the effect of the acid. The sample is dried

and screened through 4 sieve mesh and only 5g of the residue that passed

through the sieve was taken for the heavy mineral separation. Bromoform is

the only available heavy liquid that could be used (although it is a toxic liquid

and must be used with caution; a more environmental friendly and non-toxic

liquid, sodium and lithium poly tungstate is here recommended). The

bromoform was held up in a separating funnel which was already set-up on a

retort stand. A beaker was held below the separating funnel which had a

filter paper shaped in the form of a cone and held in a funnel to trap the

heavy mineral as they are let out of the separating funnel. The tap of the

separating funnel was initially closed, and the prepared sample was emptiedinto the separating funnel and stirred vigorously with a stirring rod. The

quartz and other lighter minerals float while the minerals with SG greater

than 2.89g/cm3 sink to the bottom of the funnel near the tap. The whole

process follows the principle of gravity settling. After all the heavies have

settled, the tap is opened to allow the bromoform flush the heavy minerals

out through the tap and collect in the filter paper and the tap is closed again.

In this way, the bromoform can be recycled and used again. The residue in

the filter paper was wetted with acetone to remove the bromoform and to

quicken drying. The heavy mineral concentrates was then mounted on slides

using DPS mountant (or some may prefer Canada balsam). The prepared

slides was left on a hot plate for fifteen seconds and then viewed under the

microscope for mineral identification and counting.

6.2.2Data Analysis and Presentation



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elongation and parallel extinction. The dominantly show variation ofthree colours pleochroism.Tourmaline: Mostly euhedral and well formed crystals commonly in awide range of colours such as pink, brown, black and green varieties; it

has strong birefringence, negative elongation and parallel extinction,the minerals is present every in good proportion.Rutile: Appears as elongate to sub-rounded to sub-angular prismaticgrains, occasionally reddish and yellowish, with extreme birefringence,positive elongation and parallel extinction.Hornblende: occur generally as green to brownish-green elongatecleavage grains with strong birefringence, moderate pleochroism andpositive elongation; inclined extinction about 5.Biotite: occurs as brown crystals exhibiting platy habit, with parallelextinction, straight cleavage and occasionally prismatic, terminating atone end in the shape of a prism.

Garnet: occurs as deep red to pink, colourless in some cases, withirregular form, the surface shows a pebbled, etched dodecahedronappearance. They are isotropic.Glaucophane: occurs as bluish violet grains, moderately birefringentand highly pleochroic, they tend to be elongate in the direction of themain crystallographic axis.Staurolite: they occur as straw yellow irregular grains, crudelyprismatic, lacks pleochroism, almost showing concoidal fracture, andmoderate birefrincence.Apatite: occur as dull white sub-rounded prism, lacks pleochroism.Kyanite: appear as colourless fragments being tabular to prismatic

with cleavage traces at right angle, grains show moderatebirefringence, positive elongation and inclined extinction (extin. angle=30).


Slide a: showing the provenance

diagnostic tourmaline (green variety)

and zircon grains

Slide b: showing a large staurolite grain

with zircon grain close to it.

Slide c: showing a collection of zircon,

garnet, tourmaline, apatite and opaque

grains


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6.3 Results and Interpretation

Grain size analysis results have proven to be of great geological significanceand here again it is not left out in the interpretation of mechanism anddepositional environment. The grain size parameters are here interpretedbased on Univariate and Bivariate analysis.

6.3.1Univariate Analysis

Here one parameter is alone used in the interpretation.

The mean size indicates a measure of central tendency or in this

scenario the average size of the sediment. Translated in terms of

energy, it indicates the average kinetic energy (velocity) of the

depositing agent (Sahu 1964). However, the average size is dependent

also upon the size distribution of the available source materials. In the

study area, it ranged from very fine grains to coarse, having an

average size in the medium grain sands.

The standard deviation measures the sorting of the sediments andindicates the fluctuation of the kinetic energy (velocity) conditions of

the depositing agent about its average velocity. Sorting has an inverse

relation to standard deviation, so if sufficient materials of different

sizes are not available to the depositing agent, all the fluctuation in

velocity cannot be recorded geologically. In the study area, on the

average the sediments are poorly sorted (1.18) but they range from


Slide d: showing provenance diagnostic

tourmaline, apatite, kyanite and opaque

grains

Slide e: showing grains of zircon

(colourless), tourmaline (green and pink

varieties), hornblende, kyanite, and

opaque grains

Slide f: showing typical deep red garnet

crystal with etched dodecahedron

outline.


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poorly sorted (1.65) to moderately well sorted (0.64). Thus the size

distribution of the source material also to a certain extent control the

sorting of the sediments.

Skewness measures the degree of asymmetry of the frequency

distribution and marks the position of the mean with respect to themedian. If the skewness is negative, the sample is coarse skewed (the

mean is towards the coarse side of the median) and the reverse is the

case for fine skewed samples. In the study area our samples ranges

from strongly fine skewed (0.52) to near symmetrical (-0.03) in nature.

The average being near symmetrical (0.033).

Kurtosis is conventionally considered as a measure of the peakedness

of the frequency curve, however, Kendall and Stuart (1958) believes

that it is not necessarily so and that the kurtosis should not be

interpreted as describing the shape of the frequency curve. Bydefinition, kurtosis measures the sorting ratio and not the peakedness

of the frequency curve. For normal distributions, kurtosis is unity,

values greater than unity indicates that the velocity fluctuations were

restricted within the central 50% of the average velocity for a greater

length of the time than normal. Although this was the case for about

four of our samples (C002, C004, C005 and C009), the rest fall below

unity except sample C003 which attained unity (normal distribution).

On the average the samples are describes as mesokurtic, while they

range from very platykurtic to very leptokurtic.

The table below summarizes the univariate analysis for the individualsamples from the study area.

Table 6.7: Summary for the interpretation of UnivariateAnalysis

SAMPLE NO MEAN SORTING SKEWNESS KURTOSIS

C001 Coarse Sand Moderately Sorted Strongly fine Skewed Mesokurtic

C002 Very coarse Sand Moderately well Sorted Strongly fine Skewed Very Leptokurtic

C003 Coarse Sand Poorly Sorted Near Symmetrical Mesokurtic

C004 Medium Sand Poorly Sorted Near Symmetrical Leptokurtic

C005 Medium Sand Poorly Sorted Coarse Skewed Mesokurtic

C006 Coarse Sand Poorly Sorted Coarse Skewed PlatykurticC007 Medium Sand Moderately well Sorted Coarse Skewed Very Platykurtic

C008 Medium Sand Poorly Sorted Near Symmetrical Platykurtic

C009 Fine Sand Poorly Sorted Coarse Skewed Very Leptokurtic

C010 Medium Sand Poorly Sorted Coarse Skewed Very Leptokurtic

C011 Coarse Sand Poorly Sorted Fine Skewed PlatykurticC012 Medium Sand Poorly Sorted Coarse Skewed Mesokurtic

C013 Coarse Sand Poorly Sorted Strongly Fine Skewed Platykurtic

C014 Medium Sand Poorly Sorted Nearly Symmetrical Mesokurtic



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C015 Coarse Sand Poorly Sorted Nearly Symmetrical Very Platykurtic

Every environment can be seen to have its characteristic energy conditions

and fluctuation through space and time (Sahu, 1954), the preservation of

these fluctuation is subject to availability of sufficient amount of sourcematerials (sediments) of all sizes; only then can the size distribution help

accurately to indicate environment of deposition. From the analysis above

the sediments from the area is best described as being very fine to medium

grained, poorly sorted, near symmetrical mesokurtic sands. This description

if common to river sands whose energy is typical to that of the fluvial

depositional environment.

6.3.2Bivariate AnalysisWhen two statistical parameters are combined to interprete the depositional

environment, we describe the analysis as bivariate analysis. This has

successfully being used by Friedman (1961) and Visher (1969).

#

# #

##

#

#

#

# #

#

#

#

#

#

#

RIVER SANDS

BEACH

SANDS

0.8

EXPLANATION

0.2 0.4 0.6 1.0 1.2 1.4 1.6

-0.40

0.2

0.0

0.2

0.4

0.6

C001

C002

C013

C011

C003

C008

C004

C015C014

C010

C012C009

C007C005C006

Figure 6.6: A bivariate plot of Skewness (SKI) vs Sorting (), after Friedman(1967)



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Notice that all the plot fell on the river sands field of the plot, this goes to

confirm the Univariate analysis that the sediment are from a fluvial

environment. From the field relationship, within the Basement Complex the

only medium of transportation of the sediments weathered from the rocks isthe river and surface run-offs predominantly, less of wind and dune

mechanism maybe nil.

#

#

#

###

# #

##

#

##

50

0

50

.00

.50

.00

.50

2

1

13

7

9

6

153

5

11

10

4

14

Figure 6.7: A bivariate plot of Mean (Mz) vs Sorting (), Friedman 1969.

This plot of the mean sizes against the sorting shows the point all clustering

in a particular area except samples 2 and 9. This shows that the same

mechanism of deposition was common to all the samples for them to plot in

like fields.



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6.3.3Significance of Heavy Mineral AssemblageThe significance of heavy minerals to solving geologic problems associated

with provenance and stratigraphic horizon discrepancies cannot be over-emphasized. A total of about 2289 heavy minerals were counted in the studyarea with a total of 1137 non-opaque minerals whose properties we studied.The dominant mineral in the area studied was zircon (22.4%) which wasfollowed by Tourmaline (18%) and then Rutile (13.1%). These three mineralsincidentally are regarded as the ultrastable heavy minerals and hencebecause of this Hubberd (19) successfully used them to determine thematurity of sandstones based on their indices. The ZTR index of the studyarea was calculated and seen to range from 52% to 70% among the samplesanalysed. The ZTR index was determined using the relationship given below:

ZTR index=Z+T+RNon-Opaque

A ternary diagram having zircon tourmaline rutile as apices was plottedand it had all the samples plotting in the same field indicating the sameenvironmental conditions affecting them, and that there are from the samesource.

#

##

##

#

Z

R

o

oT

100

Z

100

R o

S10

S02

S04S05

C06

S03

Figure 6.8: A ternary diagram of Zircon, Tourmaline and Rutile from thestudy area.



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The maturity of sandstones can be determined with the combined studies of

quartz crystals which was not analyzes in our study, however, from the

values obtained, it shows that the samples although they have not travelled

far from their source, they are at the verge of attaining maturity. Kyanite and

glaucophane were the least represented in the counts, but their presencecombined with the predominance of zircon (a rock that occurs in both

igneous and metamorphic terrain) and tourmaline, points to the rocks of the

Basement Complex as the source of these clastics and judging from the well

formed crystals and sub-angular crystals observed, it can be said that the

grains have nit travelled too far from their source (i.e. the provenance can be

traced to the vicinity if the rocks of the basement complex.)

Feo-codecido (1955) presented a table that characterizes the heavy mineral

suite one may likely find in certain environments.

Table 6.8: Heavy mineral associations and provenance.Associations source

Apatite,biotite,brookite,hornblende,monzonites,muscovite,rutile,titanite,tourmaline(pink variety),zircon

Acid Igneous Rocks

Cassiterite,dumortierite,fluorite,garnet,monazite,muscovitetopaz,tourmaline(blue variety),wolframite,xenotime

Granite pegmatites

Augite,chromite,diopside,hypersthene,ilmenite,magnetite,olivine,picotite,pleonaste

Basic igneous rocks

Andalusite,chondrodite,corundum,garnet,phlogopite,

staurolite,topaz,vesuvianite,wollastonite,zoisite

Contact metamorphic

rocksAndalusite,chloritoid,epidote,garnet,glaucophane,kyanite,sillimanite,staurolite,titanite, zoisite-clinozoisite

Dynamothermalmetamorphic rocks

Barite, iron ores, leucoxene, rutile, tourmaline (roundedfragments), zircon(rounded fragments)

Reworked sediments

6.4 Conclusions and RecommendationsFrom the study it has been shown that the textural parameters of surficial

sands/sediments holds a lot of history regarding the provenance and

environment of deposition of the sediments. The source of the sediments islargely from the acid igneous rocks and granite pegmatite related rocks of

the basement complex of southwestern Nigeria. The heavy mineral grains

still retain their shapes and are well formed indicating that they have not

travelled so far from their source while the results from the size analysis

show that the depositing mechanism and the environment is fluvial.



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The exercise is a rather tedious one and it is herby recommended that

before set of post graduate students embark on their field exercise, there

should be adequate materials for the analysis of their heavy minerals as that

was what led to the delay in most of our report submission. Also the issue of

electricity in the department and the school premises is so bad that notmuch can be done if all depends on power from the Power Holding Company

of Nigeria (PHCN), and finally I would recommend that the same effort or

more that the lecturers of the Department of Geology, University of Ibadan,

put into the field exercise should continue, as this would ultimately bring out

the best from the students.

REFERENCES

Feo-codecido G.1955. Heavy Mineral Techniques and their Application to

Venezuelan Stratigraphy. AAPG Bull. Vol 40/5, pp 984 1000.

Folk, R.L., And Ward, W.L.1957. Brazus River Bar: A Study of the Significance

of Grain Size Parameters. J. Sed. Petrology. Vol 27, pp 3-27.

FRIEDMANN, G.M.J. (1961). Distribution Between Dune, Beach And River

Sands From Their Textural Characteristics J.Sed. Petrology, pp 31, 514-

529.

FRIEDMANN, G.M., (1967). Dynamic Processes And Statistical Parameters

Compared For Size Frequency Distribution Of Beach And River Sands.

J.Sed. Petrology, 37, pp 327-354.

FRIEDMAN, G.M and SANDERS, J.E (1978).Principles of Sedimentology, John

Wiley and Sons, Inc, NewYork, 792p.

Galehouse J S 1971. Counting grain mounts: Number percentage vs number

frequency. Notes: J.Sed. Petrology, 22, pp 125 145.

Hubert J F 1962. A Zircon-Tourmaline-Rutile maturity index and the

interdependence of the composition of heavy mineral assemblages



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with the gross composition and texture of sandstones. J.Sed. Petrology,

32, pp 440 450.

INMAN, D L, 1952 Measures for Describing the size distribution of

sediments. . J.Sed. Petrology, 22, pp 125 145.

Kelly T E and baker C H 1966. Color Variations within Glacial Till, East-Central

North Dakota a Preliminary Investigation. Jour. Sed. Petrology. Vol

36/1.pp 75 80.

Norman C R 1969. Heavy Minerals and Size analysis of the Citronelle

Formation of the Gulf Coastal Plain. Jour. Sed. Petrology. Vol 39/4.pp

1552 1565.

Sahu B K 1964. Depositional Mechanism from the Size Analysis of Clastic

Sediments. Jour. Sed. Petrology. Vol 34/1.pp 73 83.

SATO, Y (1966). Heavy Minerals In The Sandstone Geology Monthly No. 141

Pp 34-38.

Visher G S 1969. Grain size distribution and depositional processes. J. Sed.

Petrology, 34, pp 1074 1106.



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Appendices



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C002

C003

C004 C005

C006



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C007

C008

C009

C010

C011

C012

C013

C014

C015


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