VARIABILITY IN HIGH FREQUENCY ACOUSTIC
BACKSCATTERING IN THE WATER COLUMN
A.C. LAVERY, T.K. STANTON AND P.H. WIEBE
Woods Hole Oceanographic Institution, 98 Water Street, Woods Hole MA 02543, USA
E-mail: alavery@whoi.edu
High-frequency acoustic backscattering in the water column is highly variable in both
space and time. We present selected results from a program designed to address the
origin of this variability. There are many naturally occurring processes in the water
column, of both physical and biological origin, that give rise to acoustic backscattering.
The naturally occurring spatial and temporal variability of these physical and biological
processes contribute significantly to variability in acoustic backscatter. In addition, there
is uncertainty associated with identifying and obtaining high-resolution information of
the physical and biological parameters that contribute to volume scattering. Uncertainty
in predicting volume scattering also arises from possible inaccuracies of the scattering
models, as well as variability due to speckle. Emphasis is given here to identifying the
model parameters with the highest degree of uncertainty.
1
Introduction
High-frequency acoustic scattering instruments can be used to rapidly survey large
regions of the ocean interior. The resultant data provide high-resolution synoptic
information regarding the spatial and temporal distribution of the physical and biological
processes that give rise to scattering (e.g., suspended sediments, bubbles, microstructure,
zooplankton, and fish). It is generally observed that the scattering is highly variable in
both space and time. One source of variability is the inherent speckle that arises from the
intrinsic randomness caused by summing multiple echoes with random phases. Another
significant source of variability arises from the spatial and temporal variability of the
physical and biological properties of the water-column. This naturally occurring
variability can lead to correspondingly large uncertainties in predicting volume scattering.
Furthermore, there is also uncertainty associated with 1) identifying all the processes that
give rise to volume scattering, 2) a general lack of accurate high-resolution information
of the physical and biological parameters that contribute to volume scattering, and 3) the
possible inaccuracy of the models (and associated input parameters) available for
predicting volume scattering.
In this paper, we present a selection of data from a decade-long program that
addresses many of the issues that lead to uncertainty in predicting acoustic volume
backscattering (SV). This program has involved significant model development,
laboratory measurements, and field measurements [1]. A key component to this program
is the towed sensor platform BIOMAPER-II (Bio-Optical Multi-frequency Acoustic
Physical and Environmental Recorder) designed to acquire high-resolution multi63
N.G. Pace and F.B. Jensen (eds.), Impact of Littoral Environmental Variability on Acoustic Predictions and
Sonar Performance, 63-70.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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frequency (43, 120, 200, 420, and 1000 kHz) acoustic backscattering data together with
biological and environmental information to assist in predicting volume scattering [2].
There are two identical sets of transducers mounted on BIOMAPER-II, one set facing
upwards and the other set facing downwards, designed so that full coverage of the water
column is possible even with the platform at depth. This system can be towed at constant
depth or undulated up and down (tow-yo fashion) through the water column. A video
plankton recorder (VPR) is mounted on BIOMAPER-II in order to obtain high-resolution
video images of small biological organisms present in the water column. Valuable
information regarding the orientation, size, and distribution of different organisms
(relative to the acoustics) can be obtained from the VPR [3,4]. Depth, temperature, and
conductivity, are also measured continuously (at 1/4 Hz). Together with MOCNESS [5]
net tows to acquire detailed information on species composition and size, and CTD
(conductivity, temperature, and depth) profiles, it possible to achieve a high level of
ground truthing, particularly for the biological component of the water column. We
present data and model predictions for a shallow water coastal region: the Gulf of Maine
and the waters over Georges Bank (off Cape Cod, USA).
120kHz
Longitude
Figure 1. Acoustic scattering at 120 kHz in Jordan Basin in the Gulf of Maine on R/V Endeavor
cruise 331 (December 1999). The BIOMAPER-II tow-yo track is apparent.
65
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2
Spatial and temporal variability of the physical and biological
processes in the water column
The naturally occurring spatial and temporal variability of the physical and biological
processes in the water column can give rise to very significant levels of variability in
volume backscattering data. For example, vertical variability at the spatial scale of a few
hundred meters and on the temporal scale of a day occurs due to the vertical migration of
zooplankton (Fig. 1). Superimposed on this is horizontal variability due to the size of
zooplankton patches, which can extend up to many kilometers [6,7]. Variability close to
the surface arises from scattering from bubbles due to breaking waves, with vertical
scales of a few tens of meters. In addition, we have observed elevated scattering levels at
depths that correspond to the location of the thermocline. Physical processes such as
internal waves can also give rise to elevated scattering levels (Fig. 2), with vertical scales
set by the amplitude of the wave and temporal scales set by the period of the internal
wave. It remains uncertain if the elevated scattering levels observed in the vicinity of
internal waves (and other physical processes) is a result of scattering from the physical
process itself (due to changes in the acoustic impedance), or biological organisms acting
as passive tracers. In fact, identifying all the possible processes that give rise to scattering
is a challenging problem (Fig. 3). As a consequence of the complex nature and variable
spatial and temporal scales of these processes, in order to interpret acoustics data
collected with BIOMAPER-II we have gathered significant quantities of high-resolution
ground truthing information with the VPR, MOCNESS, and CTD casts.
43 kHz
120 kHz
200 kHz
420 kHz
Figure 2. Acoustic scattering versus time (year-day) at 43, 120, 200, and 420 kHz, from a section
in Jordan Basin. An internal wave can be seen at approximately 100 m, around the depth of the
thermocline. There are two layers of elevated scattering associated with the internal wave.
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A.C. LAVERY ET AL.
Figure 3. Comparison of model predictions for a number of physical and biological sources of
scattering. The contribution to scattering from gas-bearing organisms (or bubbles) is expected to
be very significant over a broad frequency range, but particularly at frequencies close to the
resonance frequency.
3
Model accuracy and uncertainty in model parameters
Acoustic scattering models, in combination with appropriate ground-truthing information,
are critical to the interpretation of scattering data. Uncertainty regarding the accuracy of
the models can be assessed by comparison of model predictions to measurements
performed in controlled laboratory experiments. The scattering models we have
developed for zooplankton over the last decade have become increasingly more accurate,
and have been rigorously tested in controlled laboratory experiments.
To understand the variability in acoustic scattering in the water column, it is also
necessary to identify the model input parameters with the highest degree of uncertainty.
Scattering from zooplankton is highly complex and depends on parameters such as the
shape, size, orientation, material properties, and acoustic frequency. Since zooplankton
communities are typically very diverse, in order to simplify model development they have
been categorized into three groups according to their general scattering characteristics
[8]: fluid-like (e.g. euphausiids and copepods), gas-bearing (e.g. siphonophores), and
elastic-shelled (e.g. pteropods). Fueled by the naturally high abundances and general
importance of certain species of fluid-like zooplankton, much of the modeling effort has
been directed towards animals in this category [9]. Many of these models make
simplifying assumptions regarding the body shape and size. To address this, we are
currently in the process of developing scattering models for a number of animals typically
found in the water column that make use of high-resolution computerized tomography
(CT) to ascertain the shape and size of the body exterior (Fig. 4). We have compared the
predictions of a scattering model, based on the distorted wave Born approximation
(DWBA) with 3D CT measurements of animal shape as input, for decapod shrimp (which
are weak-scatterers with fluid-like material properties) to measurements of live individual
HIGH FREQUENCY ACOUSTIC BACKSCATTERING
67
(and aggregations of) decapod shrimp, with reasonable success (Fig. 5). We have found
that the target strength of an individual animal on a ping-by-ping basis depends very
strongly on the angle of orientation. For decapod shrimp, our scattering model
reproduces the data at broadside scattering better than at angles close to end-on
incidence. Typically, volume scattering averages over many animals with many different
orientations, reducing the effects due to the acute dependence on angle of orientation.
However, to accurately model volume scattering it is still necessary to obtain information
on the distribution of animal orientations in the water column, for example, through the
use of the VPR. For fluid-like zooplankton, we have found that the scattering is also
highly dependent on the material properties. Changes of only a few percent in the sound
speed and density contrasts can lead to changes in volume scattering strength of up to 15
dB [10]. In addition, there is scant information available as to the 3D distribution of
material properties within the body interior. Uncertainties in animal orientation and
material properties are the leading source of uncertainties in predicting volume scattering
for fluid-like zooplankton. For fish, uncertainties in the shape and orientation of the
swim-bladder lead to significant uncertainty in predictions of volume scattering strengths.
a
b
Figure 4. High-resolution measurements of animal shape obtained from CT scans: (a) fluid-like
Antarctic krill and (b) elastic-shelled periwinkles. The top images in each panel show the 3D
reconstruction of the outer boundary and the bottom images show representative cross-sectional
slices of the animals.
We have also developed an acoustic scattering model, and performed laboratory
experiments, for scattering from turbulent microstructure [11]. Our model includes
contributions from fluctuations in the both the index of refraction and density. The input
parameters for the model include: 1) the temperature spectrum, the salinity spectrum, and
their co-spectrum, 2) the dissipation rates of turbulent kinetic temperature, ε, and
temperature variance, χ, and 3) the acoustic frequency. We have found that our scattering
model depends very sensitively on the values of ε and χ, which are difficult to measure
without a microstructure profiler. We expect uncertainties in these parameters to
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significantly affect our predictions of volume scattering. It is also expected that other
types of microstructure, such as salt fingers, may give rise to scattering.
Figure 5. Comparison of model predictions and laboratory measurements, as a function of
orientation, for backscattering from live individual decapod shrimp at (a) 165 kHz and (b) 200
kHz. The thin solid line corresponds to data, and the thick solid line to the DWBA-based
scattering model that uses high-resolution 3D CT measurements of animal shape [12].
4
Comparison of model predictions and acoustic scattering data
We have used the data obtained from MOCNESS tows and CTD profiles to predict
acoustic volume scattering. BIOMAPER-II is typically towed at the surface during
MOCNESS or CTD casts, so it is possible to compare the acoustics data to model
predictions with the model input parameters and acoustics data collected almost
coincidentally in space and time. Scattering predictions using the models we have
developed for microstructure and fluid-like and elastic-shelled zooplankton are compared
to the acoustics data in Fig. 6 for a MOCNESS tow performed in Jordan Basin. The wellknown fluid-sphere solution to the wave equation was used to predict scattering from gasbearing zooplankton. The acoustics data at each depth were averaged over the time
period it takes to perform the net tow. Detailed analysis of the net tow revealed that small
copepods were numerically the most abundant. However, the contribution to scattering
from siphonophore gas-inclusions, called pneumatophores, is predicted to dominate
above microstructure and all other zooplankton contributions combined.
HIGH FREQUENCY ACOUSTIC BACKSCATTERING
69
Figure 6: Comparison of model predictions and data at 43, 120, 200, and 420 kHz for a
MOCNESS tow performed in Jordan Basin in the Gulf of Maine.
5
Synthesis
We have presented a selection of data that illustrate the high degree of variability in highfrequency acoustic backscattering in the water column. We have found that in order to
understand the origin of the variability it is necessary to acquire significant quantities of
high-resolution ground-truthing information about the physical and biological processes
that occur in the water column, at relevant spatial and temporal scales. We are currently
developing a new generation of scattering models for a selected number of important
fluid-like and shelled zooplankton that make use of high-resolution measurements of
animal shape and size obtained from CT scans. These models are used for the
interpretation of the scattering data. We have found that for fluid-like zooplankton,
uncertainties in the orientation and material properties give rise to the largest uncertainty
in predicting volume scattering. We have also developed a scattering model for turbulent
oceanic microstructure that includes fluctuations in both the density and index of
refraction. For microstructure, lack of high-resolution information on dissipation rates of
turbulent kinetic energy and temperature variance are the largest cause of uncertainty in
predicting volume scattering. We have compared model predictions to acoustics data
obtained at selected locations in the Gulf of Maine, and are currently working on
mapping the basin-wide contribution to scattering from zooplankton versus
microstructure.
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Acknowledgements
We thank Mark Benfield, Chuck Greene, and Joe Warren for their invaluable assistance
in collecting, analyzing, and interpreting, the acoustics and VPR data. We also thank
Nancy Copley for analyzing the MOCNESS data. This research was supported by the
United States Office of Naval Research (ONR), National Science Foundation (NSF),
National Oceanic and Atmospheric Administration (NOAA), and Woods Hole
Oceanographic Institution (WHOI). This is Woods Hole Oceanographic Institution
Contribution Number 10703.
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