SUB-BOTTOM VARIABILITY CHARACTERIZATION
USING SURFACE ACOUSTIC WAVES
MANELL E. ZAKHARIA
French Naval Academy
IRENAV BP 600 F-29240 BREST NAVAL, France
E-mail: [email protected]
The study of both acoustic propagation and reverberation require a “realistic”
information on bottom and sub-bottom properties, especially in shallow water
environment. Several methods have been proposed to study the sub-bottom structure
that are often faced to a “blurring” effect due to the strong echo corresponding to the
water-sediment interface. We propose a new approach that, unlike conventional ones,
relies on this interface as an “information carrier” and make use of the Stoneley-Scholte
surface acoustic waves, SSW. After describing briefly the properties of SSW, we will
use their velocity dispersion to recover the main properties of the sediment: density
profile, profile of shear and compression waves velocity. The investigation of the
reflection and refraction properties of SSW show that they can be treated as
“conventional” ones. A new concept of “surface wave sonar” for detecting embedded
objects as well as sediment inhomogeneities is thus possible. Several illustrations will
be given displaying results issued from tank experiment on scaled mock-ups (scale of
about a hundred).
1
Introduction
Prediction models and accurate investigation of acoustic fields require detailed
information on the bottom and sub-bottom variability mainly in the low frequency or /and
shallow water cases. Several approaches have been used to estimate the bottom
topography and the properties of the top few meters of sediment. They commonly use
conventional sonar (or seismic) systems towed in the water column and exploiting
information issued from the reflection and refraction of an incident P wave (emitted in
the water column). Most of these systems are faced with a "blurring" effect due to the
strong echo from the water-sediment interface. Several methods have been developed to
overcome this problem by using low-frequency high-resolution arrays (synthetic aperture
or/and parametric arrays for detecting embedded objects, for instance). The approach we
use in this paper is a complementary one based on the use of the seabed interface as a
"carrier of information" or a "waveguide". The corresponding system is a sonar system
towed on the bottom and using the surface acoustic waves for sediment characterization
and object detection (Fig. 1). Although such an approach can be found in some marine
seismic devices, conventional sonar processing is seldom used [1].
131
N.G. Pace and F.B. Jensen (eds.), Impact of Littoral Environmental Variability on Acoustic Predictions and
Sonar Performance, 131-138.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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MANELL E. ZAKHARIA
Figure 1. Surface wave sonar concept.
2
Velocity dispersion of SSW
The Stoneley-Scholte Waves, SSW, are heterogeneous dispersive waves guided along the
interface and possessing a vertical exponential decrease in both the water column and the
sediment [2]. They are relevant waves for sediment characterization for several reasons:
1. Their group velocity is highly dependant on the shear velocity in the sediment.
2. Their penetration into the sediment is about a wavelength (a few meters).
3. They are not affected by geometrical spreading loss (guided waves); nevertheless
they are evanescent ones (exponential vertical decrease of amplitude) and have to
be generated at the interface and detected very close to it.
Several modes could be generated [3]. In the work described, only the first-order mode
has been used (for transmitter simplicity considerations). Its energy distribution in the
water and the sediment depends on the impedance contrast. Two major cases can be
distinguished. For both cases, the major properties are summarized in Table 1:
1. Soft bottom: css (z) < cw, the shear velocity in the sediment css (z) is lower than
the sound velocity in the water cw. The energy is mostly concentrated in the
sediment but enough energy is present in the water for characterizing bottom
properties. This is the most common case for "conventional" sediments.
2. Hard bottom: css (z) > cw. SSW energy is mostly concentrated in the water.
Although the generation and detection is easier, the bottom variability is of lower
interest (rocky bottom).
Table 1. Main properties of SSW [4].
Energy
concentration
Penetration
depth
Velocity
Soft bottom
Hard bottom
in the sediment
one λ in the sediment
one λ in the water
dispersion equation
approximated by 0.8 cSS
in the water
one λ in the sediment
several λ in the water
dispersion equation
no simple approximation
SUB-BOTTOM VARIABILITY AND SURFACE ACOUSTIC WAVES
a- experiment geometry
133
b- dispersion law (experimental results)
Figure 2. Velocity dispersion of SSW.
As the penetration depth of SSW depends on the frequency, each frequency bin
carries some information on a corresponding depth layer; when using wideband signals,
several propagation depth are investigated and the group velocity dispersion of the SSW
depends on the velocity profile in the sediment. The group velocity can be easily
measured on a time-frequency representation of wideband transmitted signals [5].
Figure 2b shows a typical example of velocity dispersion of SSW. Full squares
represent theoretical data and hollow ones experimental results. The sediment is modeled
by 1-cm PVC layer with constant density and linear profiles of velocity (Fig. 2a):
cl (z)= 2090 + 101z, cs (z) = 778 + 20 z; (c in m/s and z in mm).
The velocity profiles have been measured (with an accuracy of 5%) and the
experimental profiles have been introduced in a numerical model for predicting velocity
dispersion. Bias of ± 5% have been added to the data in order to take into account the
input error. The two model outputs (with ± 5% error) have been plotted as full squares.
An experiment was conducted using the PVC layer over a marble substrate [6]. A shear
wedge generator and a point receiver were used. For two positions of the receiver, the
wideband signals were analyzed using the Wigner-Ville time-frequency distribution [5]
from which, the group delay was extracted and the velocity dispersion computed and
plotted (hollow squares, with an error bar of 5%). Figure 2b shows that the match
between the predicted and the computed data is better than a few percents.
3
Inversion
The inverse problem consists in determining the sediment properties from SSW
properties. We proposed a new method based on the use of an artificial neural network
ANN [5,7]. For computation time reasons, we show results only for a simple benchmark
including a single uniform layer. In this case, the number of parameters to recover is
reduced to a few: thickness, shear velocity, compression velocity and density (for N
layers, it would be 4N parameters). The method can be summarized as follows:
1. Several simulations of a benchmark case are realized for various input data
values; a few frequency bins of the dispersion curve are used (typically 7).
2. A random data selection (frequency bins and geoacoustical properties) is used for
training an artificial neural network (ANN).
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MANELL E. ZAKHARIA
3.
The ANN input is then reduced to the frequency bins (no more geoacoustical
data) and the network is asked to classify the input vector. While doing the
classification, an error minimization is achieved and the values corresponding to
the minimal error are extracted and compared to the actual ones. The operation is
repeated several times to reduce the influence of training set.
In order to solve the inverse problem, very loose a priori information needs to be used
(as opposed to methods such as conjugate gradient). An example is presented in
Table 2.
Table 2. A priori information for solving the inverse problem
Material
Thickness
Shear
velocity
Compression
velocity
Sediment
Substratum
m
3≤h≤19
semi ∞
m/s
140≤ css≤460
3850
km/s
1.7 ≤ cls ≤ 2.9
6.3
density
1.4 ≤ ρ ≤ 2.2
2.7
Only five values were used for each parameter leading to 625 realizations of the direct
problem. Half of the data (randomly selected) were used for training the ANN and the
other half for inversion. The results of data inversion (simulation) are given in Table 3.
Table 3. Error on the inversion of sediment properties.
parameter
Thickness
error
εh < 5%
Shear
velocity
εcs < 2%
Compression
velocity
εcl < 18%
Density
εd < 17%
For tank experiments, a similar approach was applied: several simulated realizations of
the direct problem were used for ANN training and the ANN tried to classify data issued
from the experiment (estimated group velocity). Similar accuracy was obtained: shear
velocity: ε< 0.5%, thickness: ε< 11%, other parameters: ε< 20%.
These results show the relevance of SSW for describing the sediment properties and
detecting their small variations. In order to investigate the performance of a SSW based
sonar system, we have studied the reflection and refraction properties of these waves.
4
Reflection and refraction of SSW
In the case of solid-solid interface (such as the presence of a rock in a sediment), the
reflection and transmission coefficients of SSW were investigated. As SSW are
evanescent waves (two components), two hypotheses have been found in the literature
(theoretical work) concerning the continuity conditions at the interface: continuity of each
component of SSW or continuity of their resultant. Several experiments were run using
various materials possessing impedance contrast close to the one encountered in the
seabed. Figure 3 shows the reflection of an SSW in the case of a PEHD/Polyester
interface (two bonded solids) with the following properties:
• PEHD: ρ=920±50 kg/m3, cl=2000±30 m/s, ct=1000±30 m/s, cs=760±30 m/s
•
Polyester: ρ=1120±50 kg/m3, cl=2480±30 m/s, ct =1230±30 m/s, cs=950±30 m/s
SUB-BOTTOM VARIABILITY AND SURFACE ACOUSTIC WAVES
135
Figure 3. Reflection and refraction of SSW at solid-solid interface. Vertical: time axis, horizontal:
receiver position (for a normal incidence transmitter).
From experiments, we have found that the second hypothesis (continuity of the resultant)
matches better the results and that, at oblique incidence, SSW follow laws similar to the
Snell-Descartes ones [8]:
1. SSW is reflected as a SSW (with same velocity).
2. SSW is transmitted as another SSW (with a SSW velocity corresponding to the
second medium).
3. A critical angle was observed (similarity with the case of compression waves).
4. No other waves (mode conversion) or components could be observed.
Thanks to these properties, the propagation of the SSW can be interpreted in a simple and
conventional manner. Even for 3D geometry, one can easily talk about reflection,
diffraction, directivity pattern, ray description, SSW beam forming, SSW tomography,
etc.
5
SAW sonar
The first sonar application we have investigated is the detection of embedded objects
using a surface acoustic wave sonar approach (Fig. 1). Several tank experiments have
been carried out on buried targets in a homogeneous resin [9]. The impedance contrast
has been chosen to be as close as possible to real conditions as shown in Table 4.
Table 4. Impedance contrast at sea and in tank.
SEA
Sediment: Z = 2.5 Mrayls
Rocks: Z = 14 Mrayls
Impedance contrast: 5.6
TANK
Polyester resin: Z = 3 Mrayls
Metal: Z = 46 Mrayls
Impedance contrast: 8
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MANELL E. ZAKHARIA
Figure 4. Energy scattering by a buried
sphere. Shadow effect similar to sidescan
sonar case. Sphere (16 mm); frequency 100
kHz, λs: 10 mm. Scale: 10 x 10 cm. 2
dB/color
Figure 5.
SSW sector scanning sonar.
Imaging of a buried solid sphere.
Sphere (16 mm); frequency 100 kHz,
λs: 10 mm. Scale: 10 x 10 cm. 3 dB/color
SSW transmission was achieved using a periodic excitation of the water-resin interface
(comb transducer centered around 100 kHz, scale factor: about 100). SSWs were clearly
identified by their velocity and by their vertical exponential decrease. Figures 4 and 5
show experimental results for a sphere of 16 mm (about 1.5 λs). In Fig. 4, for a given
transmitter position, the surface was finely scanned and the SSW energy on the interface
was computed. In this figure, one can see from top to bottom:
1. The incoming wave loosing some energy while propagating; the source position
is 10 cm above the top of the figure.
2. An interference zone (between the incident signal and the sphere-reflected echo).
3. A shadow zone after the sphere, similar to the one encountered in sidescan sonar.
Similar results were obtained with several other embedded objects [9]. In all cases, the
presence of an object was clearly defined by SSW energy scattering even for a small
sphere (diameter = 0.4 mm ≈ 0.4 λ) where shadow contrast was better than 10 dB.
Figure 5 shows an example of experimental results for a monostatic sonar
configuration. A sector scanning approach was used:
A transmitter with low directivity index (aperture ≈ 60°, range to target: 10 cm).
A fixed receiving array of hydrophones (broadside configuration at 2 cm from
the target, array length: 8 cm ≈ 8 λ).
3. A digital beamforming for sector scanning (Hamming array shading).
1.
2.
Figure 5 clearly shows the possibility of using SSW in sector scanning surface wave
sonar. The 3 dB resolution (white color) is comparable to the target size (extended on
each side, due to the limited array resolution). Comparable results were obtained for
various targets in other geometrical configurations for both broadside and endfire arrays
(endfire arrays are easier to tow and to handle at sea).
SUB-BOTTOM VARIABILITY AND SURFACE ACOUSTIC WAVES
6
137
Tomographic reconstruction of SAW velocity
Several applications such as the survey of hazardous areas in offshore (continental slope
stability), the survey of harbor sedimentology, the prediction of acoustical propagation,
etc. require a permanent survey tool. The tool could be sitting on the bottom and
monitoring the changes in some sub-bottom properties [8,10]. For such a purpose, we
have investigated the ability of SSW to provide insight description of the seabed from
remote distance. A transmission tomography approach was developed and applied to the
characterization of changes of sediment properties. A tomographic experiment has been
conducted in a tank using a cylindrical inclusion in a homogeneous resin. It simulates the
effect of gas migration through the sediment (gas hydrates in hazardous areas). The
cylindrical inclusion of polyester was inserted in a homogeneous resin (PEHD). The
properties of the SSW waves in both cases are the following:
• Polyester: cs = 960 ± 30 m/s, Zs = 1.07 ± 0.07 Mrayls
•
PEHD:
cs = 760 ± 30 m/s, Zs = 0.7 ± 0.07 Mrayls
Both transmitter and receiver were moved along a circle in angular steps of 10° for the
transmitter and 5° for the receiver. Conventional transmission tomography algorithms
were developed based on the measurement of time of flight. Figure 6 shows an example
of reconstructing velocity values in the mock-up using the back-propagation method. The
error on the substrate velocity is very low (2%). Although the error on the inclusion is
higher (18%), the shape of the cylindrical insert is quite well reconstructed. Other
methods (such as SIRT) provided comparable results.
Figure 6. Tomographic reconstruction of a cylindrical inclusion in a homogeneous substrate.
138
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MANELL E. ZAKHARIA
Conclusions
This paper shows that, like conventional p or s waves, SSW can be used for detailed
description of the seabed: velocity profile estimation, sector scanning sonar, detection of
embedded objects, tomographic reconstruction of sediment inhomogeneities, etc. The
concepts presented have been illustrated by several tank experiments showing the
feasibility of surface acoustic wave sonar. Effort is presently put on the development of
an efficient single mode generator at sea, using preferably contact-less devices.
Acknowledgements
This work was supported by the European Commission and the French Ministry of
transport. The author thanks Jérôme Guilbot (Total-Fina-Elf), Edouard Mouton (SageGeodia) and Emmanuelle Chauvet for their contribution.
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