Bottom Classification - BioSonics, Inc

Bottom Classification 

Janusz Burczynski, 

BioSonics Inc. 

CONTENTS 

1.INTRODUCTION 

2. METHODS OF BOTTOM CLASSIFICATION 

2.1. Averaging, Filtering and Clustering of Data 

2.2. First Echo Normalization (Cumulative Energy Curves). 

2.3. First/Second Bottom Ratio 

2.4. First Echo Division Method 

2.5. Fractal Dimension method 

2.6. Averaging and filtering of data 

2.7. Clustering of data using Fuzzy C-Means Clustering 

2.8. Sediments Layer 

2.9. Survey Maps 

3. REFERENCES 

APPENDIX: FRACTALS 

Bottom Classification6.doc page: 1

1. INTRODUCTION 

Before the invention of digital echosounders and processing methods, experienced fishermen were able to 

distinguish between a hard and soft bottom trace on an echosounder chart recorder. 

Figure 1. Example of hard and soft bottom traces on a black and white chart recorder. 

The information about bottom type, bottom sediments and aquatic plants is encoded in the echo signal. The 

signal can be stored and acquired simultaneously with GPS data. The echo signal can be decoded and 

information about the bottom can be projected onto a digital chart. 

In order to verify the results, either physical sampling of the bottom should occur or observation by divers or 

underwater camera should be recorded as acoustic data are acquired. Once verified, the results are saved so that 

unknown bottom types may be compared with this known and verified data set. 

The echo signal parameters depend on the type of bottom (in particular its hardness and roughness), but they 

also depend on the equipment parameters (i.e. frequency, transducer’s beamwidth and others). Therefore, the 

verified results will be valid only for the specific acoustic system that was used for verification. 

The methods of bottom classification implemented in BioSonics Bottom Classifier VBT are discussed in this 

paper. The objective is to help users to understand the physical principles of bottom classification methods 

implemented in the VBT software using a simple intuitive approach without using complex mathematics. 


2. METHODS OF BOTTOM CLASSIFICATION 

2.1. Averaging, Filtering and Clustering of data 

Bottom echo parameters vary widely from ping to ping. Because of this variability, we filter data and take an 

average value of bottom echo parameters over a number of pings. 

E 1 

E 1 

F D 

F D 

Figure 2. Example of data clusters for different bottom categories: E1 (first part of energy of first bottom echo) 

and FD (fractal dimension of echo envelope). Scatter plot of data shown on the left, data clusters with borders 

shown on the right. 

Echo signal parameters are displayed in x/y coordinates and bottom echoes are analyzed by clustering data that 

corresponds to different bottom categories (Figure 2). Clusters and cluster borders are computed for acoustic 

data files that are verified with physical observation of, bottom (ground truthed data). Then survey data for 

unknown bottom categories are classified according to the cluster rules; if a data report fits within a specific 

cluster border, then it is classified under this particular category (e.g. mud). If the data report does not fall 

within any of cluster borders, then the bottom is classified as unknown. 

Clusters can be computed in two or more dimensional space, however echo signal parameters x, y, z…etc., 

should not be correlated. 

In the VBT software package, we classify bottoms using two parameters, but data are taken for more than two 

echo signal parameters. 

2.2. First Echo Normalization (Cumulative Energy Curves). 

One can expect that the hard bottom will produce a sharp bottom echo with high amplitude while a soft bottom 

will produce an elongated echo with lower amplitude. These phenomena can be observed on the oscilloscope 

and echogram of the echosounder when sampling hard and/or soft bottom during surveys. 


Figure 3. Different shapes for hard and soft bottom echo; a) Echo Signal amplitude and b) Cumulative energy 

curve. 

Figure 3 a) presents examples of echoes from both hard and soft bottom. Let us square the echo amplitude, 

integrate the echo amplitude squared and then make a cumulative curve of the bottom echo integral (see Figure 

3 b). There will be a different and distinct shape between cumulative energy curves of signals from hard and 

soft bottom. The hard bottom will produce a curve with a sharp increase while the soft bottom will produce a 

curve that increases with a relatively low slope. We can acquire the bottom echoes from a known bottom type 

and save the cumulative energy curves into a database. Then, for unknown bottom types we can implement the 

“curve fitness algorithm” and recognize the bottom type according to the shape of the cumulative energy curve. 

Pouliquen and Lourton (1992) developed this method of bottom classification. It is the B1 method implemented 

in the VBT software. 

2.3. First/Second Bottom Ratio 

Figure 4 presents simplified principles of the formation of the first bottom echo. Figure 5 shows the formation 

of the second bottom echo. Following Chivers (1990), we will introduce the “hardness” and “roughness” 

signature of the bottom echo and by estimating these signatures we will classify the bottom type. 


Figure 4. Formation of the first bottom echo. 

The initial part of the first bottom echo is caused by the first reflection off a surface perpendicular to the 

transducer axis. This part of an echo (specular and coherent) is very sensitive to the pitch and roll of the vessel 

and the transducer. The remainder of the first bottom echo is caused by an oblique back reflection (noncoherent) 

and is less sensitive to pitch and roll. The first bottom echo relates mainly to its roughness signature 

and the roughness of the seabed will be enhanced by the second part of the first bottom echo (oblique 

reflection). 

The second bottom echo is produced by a double specular reflection from the seabed and a single reflection 

from the surface of the water. For a flat bottom, the specular reflection will be directly related to the hardness of 

the bottom. The initial part of the second bottom echo will be enhanced by the hardness of the bottom. If the 

bottom is rough, then the roughness will have a diminishing effect on the second bottom echo. The second 

bottom echo will be carrying mainly the hardness signature with roughness diminishing it. 

Figure 5. Formation of the second bottom echo. 


Bottom scattering is a complex physical phenomenon. Movement of the vessel has an effect on the convolution 

of the transducer beam pattern function and the oblique back reflection function of the bottom. If we extract 

from the first and second bottom echo components of an echo signal related to the hardness and roughness of 

the seabed, then the clusters boundaries (rectangular fields) representing different bottom categories can be 

established in x/y coordinates (see Figure 6). The hardness signature is estimated by taking the integral of the 

echo envelope of the second bottom echo (E2) squared and the roughness signature is estimated by taking the 

integral of the second part of first bottom echo (E1). 

Figure 6. Projection of Hardness (E2) and Roughness (E1) signatures in the VBT software and classification 

fields (clusters) in x/y coordinates. 

This method of bottom classification using the first and second bottom echo energy integral was developed and 

published first by Orlowski (1984). Additional research was done by Chivers (1990). The method was 

commercialized first by Rox-Ann. This is the B2 method implemented in the VBT software. 

2.4. First Echo Division Method 

Hardness and roughness signatures can be extracted from the first bottom echo. The following are distinct 

phases of the bottom echo formation (see Figure 7): 

• Phase 1 - Attack - from the moment the pulse reaches the bottom until the time when the bottom is 

reached by the back slope of the pulse. 

• Phase 2 - Decay - from the end of the attack phase and lasting until the time when the front of the pulse 

reaches the boundary of the ideal beam pattern. 

• Phase 3 - Release - lasting until the time when the pulse completely enters the bottom. 


Phase 1 

Phase 2 

Phase 3 

Figure 7. Succeeding phases of the sounding pulse propagation for an ideal beam pattern. 

During Phase 1, the echo is caused by a surface reverberation; the reverberation area is circular with the 

diameter increasing with time (time equal approximately to pulse duration). Phase 2 includes both surface and 

volume reverberation. During Phase 3, the volume reverberation determines the bottom echo and the 

reverberation area has the shape of a doughnut. 

After the release phase is complete, in the case of an ideal beam pattern, the only source of the echo can be the 

reverberation due to bottom volume inhomogenity. This can be observed especially in the case of soft bottom 

types, for which the main part of the echo energy penetrates the bottom. The example in Figure 8 shows the 

model echo envelope generated by a soft bottom and Figure 9 shows the model echo envelope generated by 

different types of bottom (fine sand, sand, gravel and rock). 

0.003 

Is( t) 

Iv( t ) 

Is( t) 

Iv( t) 

0.002 

0.001 

0 

67 68 69 70 71 72 

1000. 

t 

Figure 8. Results of echo envelope simulation for soft mud using an ideal (cone) beam pattern of 26° width. 

Bottom surface reverberation [Is(t)] is shown as a short dash, bottom volume reverberation [Iv(t)] as a long 

dash, and the sum [Is(t) + Iv(t)] as a line. 


0.008 

0.006 

0.004 

0.002 

0 

67 68 69 70 71 72 

1000 . t 

Figure 9. Results of echo envelope simulation for different types of bottom as measured by a 26° transducer: 

fine sand (line), sand (short dash), gravel (long dash) and rock (alternate long and short dash). 

The hardness signature is defined in the VBT software as the “energy of the first part of the first bottom echo” 

(E1), which lasts for the duration of the transmitted pulse. The roughness signature is defined as the energy of 

the “second part of the first bottom echo”, which is measured after the first part for a duration equal to 3 times 

the transmitted pulse width. The energy of the first and second part of the bottom echo is estimated by 

integrating the bottom echo amplitude squared. 

Figure 10. First bottom division method. Division of the first echo, and hardness (E1’) and roughness (E1) 

signature in x/y coordinates. 

Figure 10 shows the division of the first bottom echo (left side). The first part of the echo is represented by the 

unfilled portion of the envelope and the second part by the portion filled with diagonal lines. It also shows the 

hardness and roughness signature in x/y coordinates (right side): energy of the first part of the first bottom echo 

(E1’) and energy of the second part of the first bottom echo (E1). The different fields (rectangles) correspond to 

different bottom categories (mud, sand, and rock). 

This method of estimating roughness and hardness signatures using the first echo division is implemented in 

VBT as the B3 method. 

2.5. Fractal dimension method 

According to Euclidean geometry, a simple geometrical form can have dimension of 0 (zero: point), 1 (line), 2 

(surface), or 3 (volumetric geometrical figure). Geometric forms that display irregularities can be described as a 

form of fractal dimension objects (FD). By classifying the echo envelope in terms of its fractal dimension, we 

define the shape of the envelope by associating it with a FD number. Since the echo envelopes associated with 

different bottom types show regularities in shape, one can expect that we can classify the bottom echo in terms 

of FD. 


In the VBT software, the Fractal Dimension is a measure of the irregularity of an echo envelope obtained from 

the bottom. The echo is displayed in coordinates E1/FD (second part of first bottom energy versus Fractal 

Dimension). The fields for different bottom types are determined by means similar to the previous two methods 

(see Figure 11). This is the B4 method implemented in VBT. 

Figure 11. Fractal dimension method. The division of first bottom echo is shown on the left: the first part of the 

echo is represented by the portion of the envelope not filled and the second part by the envelope filled with 

diagonal lines. The Hardness signature and Fractal Dimension is displayed in x/y coordinates on the right (E1, 

energy of the first part of the bottom echo and FD, Fractal Dimension); three fields for different bottom 

categories are displayed (mud, sand, rock). 

2.6. Averaging and filtering of data 

The bottom echo displays a great deal of variability from ping to ping. In order to differentiate bottom echoes 

for different bottom categories we average a certain number of echo signals and produce a report from the 

averaged data (e.g. 20 pings averaged per one output report). 

The best bottom data are produced by a specular reflection from the bottom (perpendicular direction of 

transmission). A filter in the VBT software estimates energy per a specified number of pings. The strongest 

(highest energy) and weakest echoes are estimated for each report. It is assumed that an echo with the highest 

energy level is produced by specular reflection and is best for bottom classification, while the echo with the 

lowest energy level is produced by an oblique reflection and is worst for classification. 

A dialog box in the VBT filter allows the user to define the parameters for averaging and filtering the bottom 

echo signals with only higher energy levels. For example, the user may choose that only echoes with an energy 

level equal to or higher than 80% of the energy from the strongest echo will be used for data processing. 

2.7. Clustering of data using Fuzzy C-Means Clustering 

The VBT software version 1.5 includes the FCM (Fuzzy C-Mean Clustering) algorithm for automatic 

classification of fields for different bottom categories in x/y coordinates for methods B2, B3, B4. 

Fuzzy C-Means (FCM) is a data clustering technique wherein each data point belongs to a cluster to some 

degree that is specified by a membership grade. This technique was originally introduced by Jim Bezdek 

(1981) as an improvement on earlier clustering methods. It provides a method to group data points that populate 

some multidimensional space into a specific number of different clusters. The FCM algorithm starts with an 

initial guess for the cluster centers, which are intended to mark the mean location of each cluster. The initial 

guess for these cluster centers is most likely incorrect. Additionally, FCM assigns every data point a 

membership grade for each cluster. By iteratively updating the cluster centers and the membership grades for 

each data point, FCM iteratively moves the cluster centers to the “correct” location within a data set. This 

iteration is based on minimizing an objective function that represents the distance from any given data point to a 


cluster center weighted by that data point’s membership grade. Figure 12 shows clustering of Fractal Dimension 

data. 

Figure 12. Fuzzy C-Means Clustering technique for different bottom categories. Fractal Dimensions method B4 

for data files from VBT Catalogue: mud120.dt4, sand120.dt4 and rock 120.dt4. 

2.8. Sediments Layer 

A backtracking bottom tracking method is used in the VBT software. In this method, the software tracks the 

bottom from the far end of the received echo and moves towards a shorter range. Then, the bottom tracker locks 

to the rising edge of the first bottom echo. The sediment layer is located at the range above the bottom echo 

(i.e., it is the layer above the bottom). The sampling gate in VBT labeled “sediment layer” is open for sampling 

either sediment or bottom plant echoes. The VBT software estimates the thickness of this layer and the integral 

of the echo. 

2.9. Survey Maps 

Survey maps displaying the location of each VBT report acquired with GPS data can be displayed on the 

screen. Different bottom categories are color coded at the same time (see Figure 13). The output report of the 

VBT is produced in ASCII format. It provides the following information: date and time, report number, latitude, 

longitude, depth to the bottom, bottom category (coded as a number) and the energy estimated by calculating 

the integral of different parts of first and second bottom echoes. 


Figure 13. Survey map with different color-coded bottom categories estimated using Fractal Dimension 

method. 

REFERENCES 

Bezdek, J.C. 1981 Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York. 

Bakiera D. and Stepnowski A. 1996. Method of the sea bottom classification with a division of the first echo 

signal, Proceedings of the XIIIth Symposium on Hydroacoustics, Gdynia-Jurata, pp 55-60. 

Chivers R.C., Emerson N. and Burns D.R. 1990. New acoustic processing for underway surveying. The 

Hydrographic Journal, 56, 8-17. 

Mandelbrot, B. B. 1982. The fractal geometry of nature. Freeman, San Francisco. 

Orlowski, A. 1984. Application of multiple echo energy measurements for evaluation of sea bottom type. 

Ocenologia, 19, 61-78. 

Pouliquen E., and Lurton X. 1992. Sea-bed identification using echosounder signal. European Conference on 

Underwater Acoustics, Elsevier Applied Science, London and New York, p. 535. 

Stepnowski A., Moszyñski M., Komendarczyk R. and Burczyñski J. 1996. Visual real-time Bottom Typing 

System (VBTS) and neural networks experiment for seabed classification, Proceedings of the 3 rd 

European Conference on Underwater Acoustics, Heraklion, Crete, pp. 685-690. 


APPENDIX: FRACTALS 

http://library.thinkquest.org/12740/ 

Classic geometry 

• We live in 3-dimensional space (width, length, and depth). 

• Plane (length and width) is 2-dimensional. 

• Line (length) is 1-dimensional. 

• Point is 0-dimensional. 

• We can visualize an object in each of those dimensions. 

Theoretical world of mathematicians 

• Mathematicians created 4 and more dimensional space, which is difficult to visualize. 

• Our universe can be presented as 4-dimensional space (3 geometrical dimensions & time). 

• If we can have n- dimensions (integers), why not fractals? 

Meaning of fractals 

• A defining property of fractals is self-similarity, or the repetition of patterns at all scales. 

• Another property that commonly exists in fractals is infinite complexity and detail. 

• The formal definition of fractals involves fractional dimension, which itself is somewhat complex. 

Example: Koch curve 

• Start with line. 

• Put a "kink" in the line segment, sort of making it more complex. 

• Put a "kink" in each line segment. 

• Continue this to get the Koch curve. 

• Kinks cause the line segment to grow towards becoming a plane, since it is expanding "height-wise". 

• However, this curve is not 2-dimensional. 

• It's only FD = 1.2618. 

• It is more than 1-dimensional, but not quite 2-dimensional. 



Application of fractals 

• We can represent shape of complex curve as fractal (number). 

• For bottom classification we can measure shape of echo envelope as FD.

Bottom Classification - BioSonics, Inc

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?