HYDROGRAPHIC DATA PROCESSING FOR SEABED TOPOGRAPHY ENHANCEMENT

Mohd Raffi Bin Merusin

Geomatic Engineering Department, Faculty of Geoinformation and Real Estate University Teknologi Malaysia, Skudai, Johor

E-mail: korasa89@gmail.com

Abstract

Currently, a lot of bathymetry data collections have been performed using Multibeam Echo

sounder. With the ability to observe 100 percent coverage of the seabed while meeting the

International Hydrographic Organization (IHO) specification, Multibeam Echo sounder becomes

the preferred choice among hydrographic surveyors in the bathymetry data collection. By using

a suitable software, redundancy of bathymetry data can be processed that produced a three

dimensional (3D) topography of the seabed. Data collection in this study was carried out with

the observed bathymetry data using RESON SeaBat 8124 multibeam echo sounder at Tebing

Runtuh waterfront, Nusajaya. With the help of CUBE (Combined Uncertainty Bathymetry

Estimator) algorithm that is available in Fledermaus Professional 7.2 processing software, the

confidence level and quality of bathymetry depth can be improved. Therefore, the purpose of

this study is to identify the efficiency and reliability of CUBE analysis to assist on the bathymetry

data processing. In this study, bathymetry data was processed into a 3D topography of the

seabed, by fully using the Fledermaus Professional 7.2 software. Hopefully, from this study

there will be a deeper understanding about the bathymetry data processing using CUBE

analysis and this study can be a reference for other future work on bathymetry data processing

using Fledermaus Professional 7.2 software.

Key Word: CUBE, 3D topography seabed, Fledermaus Professional 7.2.,

1.0 Introduction Hydrographic surveying is a science and technology in measurement and imaging the important parameters about seabed’s attribute, characteristic and their dynamic properties. One of the products that can transform all hydrographic information in a clear view is 3D bathymetric map. With bathymetric map, all information can easily transform in visual form such as the height of terrain seafloor based on colour, calculation of terrain slope, and also able to get a true geographic coordinate for each point on seafloor directly with respect to the nautical chart and Google Earth as a reference. The instrument that been used for collecting bathymetry data is multibeam echosounder system (MBES). The Multibeam echosounder (MBES) has provided 100 percent of the sea floor ensonification while meeting the IHO Specification. Multibeam system can produce a high resolution bathymetry data throughout the survey area. Multibeam echosounder may be considered as a series of single beam echo sounder mounted on a array. For example, in RESON SeaBat 8124, there are altogether 101 tranducer (beams), each with 1.5 degree alongtrack and acrosstrack respectively. Every ping of signal emitted will be equivalent to a fan-shape transmission which result in the receiving of 101 sounding across the track of the vessel. It is easily conceived that the accuracy of sounding using multi-beam echo sounder will deteriorate from the beam at nadir to the outer side beams because of the dynamic movement of the vessel. The most significant effect is due to the movement in roll, pitch and heave. In Fledermaus Professional 7.2, the problem of roll, pitch and heave can be solved by using sensor specific error model and the vessel configuration file. This configuration menu is only available when doing the processing hydrographic data using auto-processing CUBE (Combined Uncertainty Bathymetry Estimator). Calder and Mayer, 2003, say that, the Combined Uncertainty and Bathymetry Estimator (CUBE) algorithm is an attempt to utilise understanding of the uncertainty of soundings in a processing scheme for high-density MBES data that answers to the alternative question of depth and uncertainty. Starting with the Hare-Godin-Mayer error model for MBES (Hare et al., 1995), it attributes each sounding with an estimate of vertical and horizontal uncertainty. It then estimates the true depth at a fixed point in space, given only the noisy soundings from the data-stream and their associated uncertainties. The purpose of this study is to fully use Fledermaus Professional 7.2. on processing the hydrographic data to resulting a bathymetric map as product, then establishes a clear visual in 3D. Next section author will explain part by part in data processing until producing the product.

2.0 CUBE Editing Technique

Starting in Fledermaus Professional version 6.1, a new type of auto-processing algorithm is introduced, called CUBE processing. CUBE stands for Combined Uncertainty and Bathymetry Estimator based on research performed at the Center of Costal Mapping at the University of New Hampshire lead by Brian Calder. L. Mayer

The CUBE method is to statistically calculate the most likely height (a hypothesis) for the surface using information that user provided. This calculation takes into account the uncertainty for individual soundings, which is more certain soundings make more contribution to the surface and less certain soundings make less contribution to the surface.

When the Cube algorithm is run, a list of all possible hypotheses is generated and the algorithm attempts to choose the best hypothesis to indicates the height for the sounding bin. The chosen hypothesis is called the selected hypothesis and the other hypotheses are called the alternate hypotheses. This system simplifies the amount of work required from a human operator since the operator must only verify that the correct hypothesis is chosen in area where more than one hypothesis exists.

Figure 3.5: Two different hypotheses

Figure 2.1 show displays a possible situation where two different hypotheses would be created.

Alternate Hypothesis

Selected Hypothesis

Z

3.0 Methodology

This project is prepared follow step by step. Readers are able to figure out and follow the step of preparing this proposal starting from the beginning untill the end. It is easier to make readers understand what is the purposes of preparing this project.

3.1 Data Collection

The data for this project was obtained from hydrographic surveying at Tebing Runtuh, Nusajaya. The survey was carried out by CAT B survey team. The project was carried out day on 16 March 2012.

Figure 3.2: TEBING RUNTUH WATERFRONT, NUSAJAYA JOHOR

The most important thing of this study in the post processing procedure of raw data processing is Software and Hardware. Hardware is components used to collect the data and software is used to interpret the data. The detail about software and hardware are shown as below;

Software : Fledermaus Professional version 7.2

Hardware : Multibeam Echo sounder (Reson Seabat 8124)

Data : Raw data multibeam: QPD format

3.2 DATA PROCESSING

Bathymetric data is used in this study. This data will be interpreted by using Fledermaus

professional version 7.2 with other module in order to get the result. The steps of processing the

data can be seen as shown in figure 3.3.

Figure 3.3: Processing Step in Fledermaus professional including with other module

Launch 3D Editor

Set checked area, save

and exit

Manually deleting suspect

sounding (Sounding

editing)

Add/Delete/Override

hypotheses (CUBE editing)

Clean remaining data spike

Exit

fledermaus

Create animation merge with

geo-referencing image

Launch DMagic Unload PFM,

interpolate and rugosity

Convert raw data to

.gsf format

Applying tide correction

using FMCommand

Select data, build PFM

Launch to

Fledermaus

Open DMagic, create a

project

Add ungridded data

Auto-processing CUBE

Select sensor type, add

vessel configuration file

Select surface and

applying filter

Extract region or entire

area

Select area for cleaning

hypotheses and sounding

For editing sounding, Fledermaus Professional 7.2 provides two technique, sounding editing and CUBE editing. In this section, author would like to explain the difference propose between both technique. These cleaning techniques only available when some of area on pfm file loaded and the surface has been selected.

Sounding Editing Technique

Editing is usually performed by first selecting a set of sounding, and then rejecting, un-rejecting, setting, or cleaning a suspect flag for those soundings. While using the 3DEditor, the mouse is used for navigating through the scene and also selecting and deselecting soundings. The right-click menu also contains many options for editing and can be accessed by right clicking on the main window. Figure 3.4 below shows how the sounding editing performs on 3DEditor.

Figure 3.4: Sounding editing for selected area

Based on above picture, the diamond gold shape represent for suspect sounding or other terms spike sounding. This suspect sounding was generated by depth threshold difference, found on the menu IVS suspect sounding filters. The filter will select sounding within area that is beyond than a certain threshold away in a selected depth bin. To reject the selected suspect soundings from the data, operator simply selects all suspect soundings and clicks the button reject. Figure 3.5 below shows the outcome visual after rejecting all suspect soundings.

Figure 3.5: After rejecting all suspect soundings

Operator has to remind that, the filter only based on calculation depth threshold difference in each bin. The algorithm cannot give a hundred percent confidence that the suspect soundings generated by the algorithm are true. In fact, at some circumstance the raw soundings data is correct. So operator need to do recheck each selected suspect sounding based on comparing height to each of their bin neighbour. If it is proven as a true suspect sounding, operator gains the confidence to reject the chosen suspect sounding. Based on the figure 3.5, we can see there have a hole on the surface, marked with red colour circle. This can be resolved by continuing the editing process on next path, it is CUBE editing.

Next author would like to show how to carry on the CUBE editing processing. Based on figure 3.5, there have some hole on the surface after applying sounding editing. This is because there have some hypotheses that not correct on height. The area was pointed and zooming in order to investigate what the main causes of the hole. The zooming area is shown on figure 3.6 below.

Figure 3.6: Two hypotheses that not correct on height

Based on above figure, we can see there have two hypotheses that have problem with their true height on each bin. Operator simply easily overrides a new hypothesis by taking into consideration of number of sample and the confidence interval for each hypothesis. Number of sample means that the number of sounding that found on nears the hypothesis. Meanwhile, the confidence interval is a line that representing the 95% confidence interval for each hypothesis. To simplify it, author has been labelling each of the hypotheses as show on figure 3.6. The information of number of sample and confidence for each hypothesis interval is shown on table 1.0 below.

A1

A2

A3

A4

B1

B2

B3

Label Information Description

A1

A2

A3

A4

B1

B2

B3

Table 1.0: The information of number of sample and confidence interval for each hypothesis

Based on table above, we can define the new selected hypothesis for each bin. For bin A, author chosen the hypothesis A3 as selected hypothesis. Even the confidence interval is less than A2, but it have a large number of sample which is 17, which is greater than hypothesis A2, where the number of sample is 5.

Meanwhile, for bin B, author choose hypothesis B3 as selected hypothesis. It is because, hypothesis B3 is greater than two other hypothesis on confidence interval and number of sample. To override for new selected hypothesis, firstly select the selected hypothesis. Then right-click on the cube surface, go to edit, and choose override hypotheses. Figure 3.7 and figure 3.8 show the situation before and after override hypotheses for bin A and bin B respectively.

Figure 3.7: Comparison before and after override hypothesis for bin A

Figure 3.8: Comparison before and after override hypothesis for bin B

4.0 RESULT AND ANALYSIS

This paper describes the objective assessment on how producing a bathymetry map from start to finish fully operated on Fledermaus Professional 7.2. software. In this section author will explained the result of CUBE surface, Digital Terrain Model for seafloor, interpretation with Google earth and lastly 3D flying object.

Figure 4.1: Comparison before and after Sounding/CUBE editing on 3D Editor

Based on figure 4.1, shows the comparison of CUBE surface before and after applying sounding and CUBE cleaning method. The result can be viewed which the cube surface is clean and seamlessly. This surface is resulting from reliability of each bin of hypothesis in terms combination number of sample with confidence interval for each bin. The CUBE algorithm has made easier for operator to produce 3D seafloor by using the number of sample + neighbour disambiguation method. This method is good for shallow water lots of features.

Figure 4.2: Digital Terrain Model for Bathymetric Mapping

Based on above figure, show the formation of the seabed may be formed in 3D modelling using Fledermaus Professional 7.2. The picture of the seabed shape can be described clearly resulted from the formation of 3D bathymetry modeling. CUBE editing method can provide a more accurate depth value based on two concerns. The first selection based on a number of samples. Second, the selection based on the confidence interval for each selected hypothesis. Based on both analyzes, changes in the seabed can be described in detail with reference to the actual depth of the point obtained. Both analysis also help to speed up the process of formation 3D that will be produced. Bathymetry contour is generated at every 10 meter interval.

Technique based on rugosity, the surface of the seabed can be in the form of reality Transform to identify sediment on the seabed surface. This technique is only to the common understanding of the surface of the seabed. To get the actual image, we need to make a side scan sonar survey of the area.

Figure 4.3 shows the display for rugosity surface of seabed. The surface images similar to the image resulting from the measurement of side scan sonar. On the view, we can know the general appearance of the seabed. However, the display surface does not represent the true image of the seabed. But we can obtain information through the seabed profile and picture from the image above.

Figure 4.3: Rugosity surface for seabed

Figure 4.4: Rugosity profile surface for seabed

P1

P2

P3

P1

P2

P3

Based on Figure 4.3 and Figure 4.4, the authors found that the profiles P1 and P2 are hilly and rocky. By comparing the profiles P1 and P2 to rugosity surface, analysis of the seabed in the area is rocky and hilly acceptable. While for P3 profile, the profile shows a smooth, continuous gradient, and there is no form of hills. Therefore, the authors consider the profile near P1 is not rocky land area is hilly or sandy areas.

Figure 4.5: 3D flying ROV on real time

Real time 3d flying object can be generated on Fledermaus software. Guided by the route that has been made on Routeplanner module. ROV able to move along the course set. During the movement, the ROV can provide a positioning for each depth to seafloor and directly illustrated into depth profiling form. This statement can be referred on figure 4.5, where an ROV making further observation in terms of manifest observation along cross depth profiling of the 50 meter wide swatch respect to seafloor surface. This can facilitate the operator on depth analysis in real time.

5.0 CONCLUSION AND RECOMMENDATION

Overall, the objective of this study was successfully achieved. With editing applications available in the software 3DEditor Flerdermaus Professional 7.2, the bathymetry data processing can be implemented and produce the final product of 3D topographic survey the seabed.

Fledermaus Professional 7.2 software is useful and appropriate software to process a lot of bathymetry data. With the help of algorithm CUBE (Combined uncertainty Bathymetry Estimator) all the editing can be performed with ease and makes editing high-quality results and reliable. But I found that this technique is the difficulty, the need for the user in confirming the actual depth of the hypothesis. This is because not all errors can be resolved based on the

depth point processing automatically. But overall this technique can reduce the time users in the bathymetry data processing over manual methods in the editor box (swath editing).

Seabed surface can be studied using a combination of techniques generally interpolate process of the surface with the rugosity. However, these techniques provide only a rough understanding of the sediment found on the surface of the sea. With this technique, the user can know the sea bottom sediment without making a side scan sonar survey in the field.

The end result is a 3D study of the seabed topography. I found this very easy Fledermaus software used to produce high resolution 3D display. 3D topography of the seabed is producing a lot of interest in assisting the geological and geophysical activities to identify the substantive changes in the seabed. Fledermaus software with the ability to produce an animated display can increase confidence in the quality and depth of the seabed.

Finally, with the involvement of these studies can provide general guidance in using the software Fledermaus Professional 7.2 for processing the bathymetry data. In general, this study provides a better understanding of the software Fledermaus Professional 7.2.

Thus, the contribution of this study is to give a basic guide in processing multibeam data using new software, which is Fledermaus Professional 7.2. In general, this study will provide a better understanding of Fledermaus Professional 7.2 software.

ACKNOWLEDGEMENT

The author would like to acknowledge to Prof. Sr Dr. Mohd Razali Bin Mahmud, Mr. Hilmi Bin Abdullah and Mr. Eranda for their guidance and support for this paper.

References

Calder B and Mayer L. (2003). Automatic processing of high-rate, high-density multibeam echosounder data. University of New Hampshire

Calder, B.R (2003). Automated Statistical Processing of Multibeam Echosounder Data. University of New Hampshire.

Delf Egge (2009). Education and Training in Hydrography – Status And Perspective. Hafencity university, Hamburg Germany.

IVS training series, (2004). An Introduction to Fledermaus & 3D Visualization. New Brunswick, Canada.

Kerim G. & Jorgen A. (2007). 3D- Visualization of Fairway Margins, Vessel , Hull Versus Depth Data. Bachelor’s Thesis in Geomatics. University of Gavle.

Mallace D. & Robertson P. (2007). Alternative use of CUBE; how to fit square peg in a round hole

Mallace D. & Lindsey G. (2007). Multibeam processing- the end to manual editing?.

Razali, Rusli, Shahlan dan Usmuni (1997). Monograf Hidrografi Asas. Fakulti Kejuruteraan dan Sains Geoinformasi. Universiti Teknologi Malaysia.

Syamsul Khairul Bin Ab Ghani (2011). Acoustic Seabed Classification Using CARIS HIPS & SIPS 7.0. Sarjana Muda Kejuruteraan Geomatik, Universiti Teknologi Malaysia.

Vásquez, Miguel E. (2007). Tuning The CARIS Implementation Of CUBE For Patagonian Waters. M.Sc.E. thesis, Department of Geodesy and Geomatic Engineering, Technical Report No. 251, University of New Brunswick, Frederiction, New Brunswick, Canada, 107 pp.

AUTHOR

Mohd Raffi Bin Merusin was born in 1989. He is a B. Eng. (Geomatic) student at Universiti Teknologi Malaysia. His project focuses on the hydrographic data processing for seabed topography enhancement Contact: +60149136794