sbesmodelbased 10 november 2011

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Model-based sediment classification usingsingle-beam echo-sounder signals

applying sophisticated softwareto the simplest hardware

Mirjam Snellen, Kerstin Siemes and Dick G. Simons, JASA 129 (5), May 2011, pp. 2878-2888

Professor Dick G. SimonsChair Acoustic Remote SensingDelft University of Technology


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Presentation outline Sediment classification applications and market perspective

The single-beam echo sounder (SBES) system - classification

methodology

Modelling the echo sounder received signal - backscatter modelused

The optimization method Differential Evolution

Testing the approach on synthetic data

Application of the method to experimental data trial area and

results Conclusions


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Applications of sediment classificationmarket perspective

Coastal engineering (building at sea)

Marine biology (fisheries)

Marine geology (sediment transport)

Hydrography

Support of dredging operations

Support of mine hunting

Sonar performance modelling


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Marine geology


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The single-beam echo sounder (SBES)

Gives depth measurements based ontravel time, but

amplitude and shape of received signalscontains information on sediment composition

Gravel

Mud

Measurement


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SBES classification methodology


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Full modelling the echo signal intensity

4

4

R

b

eI t B dA

RA

with

the angle of incidence (function oft)

b() the backscattering cross section

A() the surface contributing to the

sound received at time t

B() the transmit/receive directivity

pattern of the transducer

the attenuation coefficient

Rthe slant range (function of t)


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8

Employed backscatter model b()

Backscatter modelling accounting for both interface and volumescattering ([1])

Interface scattering taken as the result of three differentapproximations:

The Kirchhoff approximation, valid for smooth to moderately rough sediments and grazingincidence angles near 90;

The composite roughness approximation, valid for smooth to moderately rough sediments andgrazing incidence angles away from 90;

Large-roughness scattering, where the scattering cross section is determined from an

empirical expression which is derived for rough sediments like gravel and rock.

[1] APL-UW High-frequency Ocean Environmental Acoustic Models Handbook,Technical Report, APL-UW TR 9407, AEAS 9501, October 1994


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Backscatter model input parameters

Isotropic relief spectrum W2(K)=(h0K)-

w2

502 log dMz

PP

R

pv

Im10ln

sin15

2

22

2 22 cosPwith and

i 1

1

Backscatter model input parameters


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Default input parameter values(fixed relations with Mz)


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Backscatter strength versus grazing angle


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Example of modelled SBES signals


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Maximizing the model-data agreement

Energy or Cost function C(fitness) quantifying the difference

between modelled and measured SBES return:

Minimization of this cost function for three parameters:

Mean grain size Mz

Volume scattering parameter 2

Strength of bottom relief spectrum w2

Differential Evolution employed as the optimization method

C


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Differential Evolution (DE) algorithmbasic principle

Diff ti l E l ti (DE) l ith


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Differential Evolution (DE) algorithmin more detail


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DE optimizationsynthetic data

Sandy gravel sG


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Muddy sand mSDE optimizationsynthetic data


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Sandy clay sMDE optimizationsynthetic data


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Overview results synthetic tests

Mz

w2

2

A li ti t i t l SBES d t


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Application to experimental SBES dataMeasurement area and trial logistics

Cleaver Bank area 38 kHz SBES system

Well-known geology

Variety of sediment types

Bottom grabs available

Water depth: 30-60 m


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Results - estimated Mz, w2, and 2 separately

mean grain size

spectral strength

volume scattering parameter


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Results

reproduced from previous slide

Using fixed relation betweenMz and w2 and 2

(see slide 10)

less realistic


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Conclusions

Seafloor classification using a model-based approach on single-beam echo-sounder data is feasible

The optimized mean grain size Mz show good agreement with the

mean grain sizes obtained from the grabs

The optimized spectrum strength w2 and volume scattering

parameter 2 are what we expect from the APL relations

Plough marks only present in the w2 map

Optimization for Mz only gives similar map, although less realistic,

but computationally less demanding

(useful for a quick assessment of the area of interest)


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SBES and MBESclassification results

Cleaver Bank, North Sea

River Waal

Oosterschelde

sbesmodelbased 10 november 2011

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