COMBINATION OF ACOUSTICS
WITH HIGH RESOLUTION OCEANOGRAPHY
JÜRGEN SELLSCHOPP
Forschungsanstalt der Bundeswehr für Wasserschall und Geophysik
Klausdorfer Weg 2-24, 24148 Kiel, Germany
E-mail: [email protected]
PETER NIELSEN
SACLANT Undersea Research Centre, Viale San Bartolomeo 400, 19138 La Spezia, Italy
E-mail: [email protected]
MARTIN SIDERIUS
Science Applications International Corporation, La Jolla, CA 92037,USA
E-mail: [email protected]
The variability of underwater sound observed on a moving platform in littoral waters is
a combination of the effects of temporal and spatial environmental variations, which are
intermingled and obscured by the change of the platform position. Sound transmission
experiments over a fixed range are an approach to separate different sources of
environmental impact on acoustic propagation. Even in a fixed geometry time
variability experiment, the most demanding observations are for the spatial distributions
of controlling parameters. On a short time scale, the variation of spatial distributions is
responsible for acoustic variability, while mean, range averaged conditions may stay
unchanged. The instrumentation for monitoring the ocean environment during the
ASCOT 01 acoustic trial included moored instruments for the measurement of
temperature and current profiles, tidal and surface waves. By means of wave dispersion
relations, moored records of ocean variability may be transformed into guessed spatial
fine structure. But critical results such as correct slope statistics of sound channel
boundaries and iso-velocity surfaces cannot be guaranteed. A 40-sensor CTD chain was
continuously towed parallel to the acoustic range. The measured 2-dimensional sound
velocity field has 2 m vertical and 4 m horizontal resolution. A primitive model, which
propagates rays through measured high resolution sound velocity fields is able to
explain observed multipath arrival structures on a vertical hydrophone array.
1
Introduction
High quality predictions of the underwater sound channel are desired, since they are the
essential input to reliable modeling of the conditions for sonar detection and underwater
communication. Environmental parameters of primary concern are the sound velocity
profile, the depth and composition of the seabed and the sea state. By these parameters,
the ocean volume and the boundaries of the littoral sound channel are covered.
Realistically none of these parameters can be prophesied on scales of kilometer or hour
orders. The best possible approach, to allow for a realistic environment in acoustic
19
N.G. Pace and F.B. Jensen (eds.), Impact of Littoral Environmental Variability on Acoustic Predictions and
Sonar Performance, 19-26.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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J. SELLSCHOPP ET AL.
model calculations, is the separation between predictable average conditions and
deterministically unpredictable environmental variability, which might however be well
described by a statistical parameter such as the surface wave spectrum. Whether small
scale environmental variability is treated adequately in acoustic modeling cannot be
decided from the comparison of sound measurements with model results, if there are
free adjustable parameters in the model, which can be tuned until the results match. If it
is impossible to measure all parameters, which have an impact on acoustics, there is
always a danger that a parameter with substantial influence is overlooked and a wrong
parameter adjusted instead. This becomes especially obvious in reverse modeling, where
the restriction on a limited set of tractable parameters forces all deviations of the
environment from an assumed predefined state to act into the selected parameter
subspace. Consequentially inversely modeled parameters may not be accurate. The
range dependence of the results from inverse modeling of bottom parameters [1] is a
hint that the treatment of environmental variability in the model is inappropriate. A
description of the ambience as accurate as possible, deduced from extensive
measurements, is required for a better connection of acoustic variability and uncertainty
to ocean variability. Whether or not the variability of a certain environmental parameter
can be finally neglected, can only be decided after sensitivity studies have been made
with realistic data applied to appropriate models.
Small scale fluctuations have a much higher impact on the littoral sound channel
than in the open ocean. In a shallow sound channel, multiple interactions with the
surface and bottom boundaries influence the energy and direction of the propagated
sound. In contrast to stationary bottom scatterers, the rough surface introduces time
dependence into acoustic signals. But also in a stratified ocean under summer
conditions, when the sound does not interact with the sea surface in motion, time
variability of the range dependent sound velocity structure in the water column is able to
significantly impact transmissions between fixed locations. Because of the difficulty of
measuring the internal variability, its effect on acoustic signals is harder to access than
that of surface waves.
In order to separate influences of different kinds, it is advantageous to perform
experiments with a fixed source and receiver, which would at least keep the range
constant and the bottom unchanged. Measurements of acoustic variability should be
complemented by measurements of environmental variables, so that the assumptions
introduced into acoustic models are minimized.
2
ASCOT 01 experimental setup
The acronym ASCOT is nicely translated into “Acoustic Scenario Connected with an
Oceanography Trial”, although it originally stands for “Assessment of Skill for Coastal
Ocean Transients”. The NATO Research Vessel ALLIANCE spent three weeks in June
2001 for a collaborative effort with Harvard University [2] in Massachusetts Bay and
half of the Gulf of Maine. Data from an initial ocean survey were used to initiate the
primitive equation forecast model of the Harvard Ocean Prediction System (HOPS). A
limited amount of data was acquired for assimilation during the forecast period. The
predictive skill experiment was concluded with a verification survey. After the first
phase, there was an opportunity to embed ocean acoustics experiments of four days
duration, interrupted by one day for logistics. The experimental setup was similar to the
ACOUSTICS AND HIGH RESOLUTION OCEANOGRAPHY
21
fixed range experiments on Adventure Bank (Strait of Sicily) two years before [1]. The
compulsory second ship was hired from the University of New Hampshire. A site with
suitable conditions, which are defined by proximity to the coast, no interference with
protected ocean areas and an approximately constant water depth of about 100 m over
10 km range, was identified 30 nautical miles north of Cape Cod, 20 miles east of Cape
Ann (Fig. 1).
Figure 1. Bathymetry, mooring positions and tow track of the acoustic test site.
The bottom was hard and rougher than expected. Soundings were taken every 4 m on a
straight line. The bottom contours of Fig. 1 were obtained by swath mapping with 20 m
horizontal resolution. At the SE end of the site, a frame was lowered to the ground with
a sound source mounted on top. A vertical line array with 64 hydrophones was deployed
at distances 5, 2, 10 and 0.78 km from the source tower, each deployment lasting for one
day. A suite of instruments was available for environmental monitoring. Three
thermistor strings, each equipped with 11 thermistors with 5 m vertical separation, were
deployed at the corners of an equilateral triangle with side length 2.35 km. While one
standard thermistor string was sampled every 2 minutes, two strings had response times
and sample rates of 10 seconds. An upward looking acoustic Doppler current profiler in
the center of the triangle was used with 5 minutes averaging interval and sample rate.
Sea surface roughness due to wind waves was continuously measured by a wave rider
buoy. A meteorological buoy measured wind close to the surface and undistorted by the
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ship. A tide gauge, which was combined with the ADCP, was out of range. Qualitatively
the measurements compare well with tides in Boston, which they precede by less than
30 minutes.
In addition to the moored instruments, a CTD chain [3] was used for the direct
measurement of spatial structures. It was deployed from ALLIANCE and handed over to
the GULF CHALLENGER for continuous coverage of a 10-km track parallel to the
acoustic range. The CTD chain configuration involved 40 packages for the measurement
of conductivity, temperature and depth (CTD), distributed over the 70-m aperture
between surface float and depressor (Fig. 2). With standard scanning cycles of 2
seconds, the spatial sampling mesh width was 3.3 m horizontally and 1.8 m vertically.
CLANT
SA
ER
EN
TR
E
UND
SE
A
C
RESEARCH
ELECTRODE
CONDUCTIVITY
TEMPERATURE
DEPTH
INSULATED
WIRE
ELECTRODE
DEPRESSOR
Figure 2. Sketch of the towed CTD chain. Sensors are inductively coupled to the towing cable.
3
Environmental variability and its impact on acoustics
In the course of a tidal cycle, the surface level changes by 2 m. The bottom moored
thermistor strings feel the rise and fall of the thermocline with the tide. The temperature
contours from low-pass filtered time series of the thermistor strings are highly correlated
with, but not strictly parallel to surface elevations (Fig. 3). Acoustic energy propagating
with low grazing angles would feel the thermocline position rather than the ocean
surface. At 10 km range, the first four or five arrivals (see Fig. 4 of Nielsen et al. [4])
are from eigenrays, which do not touch the surface, but are influenced by sub-surface
tidal signatures.
Tidal currents are approximately aligned with the acoustic track. They have
maximum strength 25 cm/s and are nearly independent of depth. The long axis of the
tidal ellipse is 2.5 km. Integrated over the 5 days of acoustic experiments, there was a
dislocation of water masses to the SE by 10 km. Water mass properties were however
sufficiently independent from horizontal coordinates, with the consequence, that
flushing did not play a role.
Tidal waves alter the sound channel as a whole, but do not change its character in
general. Higher components of the wave spectrum introduce range dependence to the
sound propagation problem. Original (unfiltered) records from the moored thermistor
ACOUSTICS AND HIGH RESOLUTION OCEANOGRAPHY
23
strings contain signatures of internal waves with periods down to a few minutes, which
would not be correctly sampled using standard instruments having response times of 2
minutes (Fig. 4). Records at different depths are well correlated. By inspection we find
no indication of internal wave modes other than the lowest. From the density profile of
the water column, the dispersion relation of internal waves was solved numerically [5].
A first mode oscillation of 1 (10) cycle(s) per hour is related to 0.6 (8.5) wave lengths
per km. Phase velocities of these short internal waves are only slightly higher than the
background current. The frequency spectrum of internal waves is therefore heavily
affected by frequencies of encounter.
Figure 3. Tidal surface elevation (upper) and filtered temperature distribution (4°C – 10°C) from
the eastern thermistor string. White bars indicate the duration of the acoustic experiments at 5, 2,
10 and 0.78 km range.
Figure 4. Temperature contours from original records of the western (left), southern (mid) and
eastern (right) thermistor strings. While with 10 s sampling rate, two strings resolve high
frequency internal waves, the third with 2 minutes under-samples.
For deterministic modeling of acoustic transmissions through a fluctuating ocean,
the time and space dependent sound velocity field of the ensonified area must be known.
Since it is impossible to obtain three or four dimensional measurements of ocean
parameters with sufficient resolution, artificial representations of the sound velocity
field are used instead with the correct fluctuation statistics in anticipation. Under
favorable conditions and reasonable assumptions such as independence from the
azimuth angle, a local frequency spectrum of internal waves may be converted into a
horizontal wave number spectrum, which in turn is used for the realization of a spatially
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fluctuating sound velocity field. Because of the tidal currents, the moored
measurements during ASCOT01 cannot be transformed into wave number spectra.
Spatial variability, directly measured by means of the towed CTD chain was
analyzed for the wave number spectrum of contour displacements (Fig. 5). Low wave
numbers, which would reflect repetitions of the 10 km track were excluded by high pass
filtering. This also removes potential contamination by temporal effects such as tides.
Figure 5. Wave number spectra of the vertical displacement of high pass filtered temperature
contour lines from 6 to 12 °C. Note that the spectral energy increases with depth. The blue curve
is for 6°C.
Figure 6. Distribution of vertical displacements of the high pass filtered 10°C contour. 6 to 12°C
distributions look qualitatively the same. The standard deviation increases with depth (decreasing
temperature). A Gaussian standard distribution is shown for comparison.
The spatial temperature records of the towed sections show pronounced downward
excursions of the contour lines similar to those in Fig. 4. The asymmetry of internal
fluctuations is reflected by the distributions of contour line depth. Throughout the
thermocline, they deviate significantly from a Gaussian normal distribution (Fig. 6).
A primitive ray tracing routine was used for the simulation of acoustic pulses
propagating through a real fluctuating sound velocity field. Measured sound velocity (by
CTD chain temperature and salinity) was interpolated to a 1 x 1 m grid in the vertical
plane spanning between source and receiver. The routine calculates sound velocity
gradients, grazing angle, vertical position and time increments at each horizontal grid
ACOUSTICS AND HIGH RESOLUTION OCEANOGRAPHY
25
point. Two realizations are displayed in Fig. 7. Bottom topography was taken from echo
sounder recordings and the sound velocity field from a parallel tow track. Only the rays
of a narrow beam are displayed, which arrive at a receiver 40 m in depth.
Figure 7. Multipath propagation through a realistic ocean. A hardly noticed horizontal shift of the
sound velocity field causes a significant change of the pattern of eigenrays.
The simple relation between the initial ray elevation angle and the number of
turning points, which exists in range independent environments with smooth boundaries,
gets lost by range dependence. Different rays of the same class can coexist, appear and
vanish by small changes in the sound velocity field. Small differences in travel time
cause frequency dependent fading. Figure 8 shows pulse arrivals in the water column at
the position of the receiving array for a range independent and for a sample range
dependent situation.
Figure 8. First few arrivals from multipath propagation over the 2 km test range. Left: Range
independent. Right: Realistic range dependent example.
Bottom roughness amplifies the effect of fine structure ocean variability. Slightly
modified refraction in the water column may shift the point of contact with the bottom
to a facet with different slope, so that differential adjustment of the eigenray path will
not bring the reflected ray back to the desired direction. With bottom roughness single
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eigenrays die and come to life much easier than with a smooth bottom (see Fig. 7).
Because multipath arrival patterns become less stable, matched field processing is a
challenging task in this environment.
4
Conclusion
Nielsen et al. [4] have shown that irrespective of a similar experimental setup the results
of the ASCOT01 acoustic experiments differ largely from the ADVENT99 results. The
matched field correlation of an early received signal with subsequent signals drops off
very fast, even for low frequencies. While source localization by common matched field
processing failed in the ASCOT01 environment, Siderius et al. [6] successfully used a
more robust processor, which correlates signal envelopes separately for each
hydrophone.
The unexpected resistance of the ASCOT01 environment against standard data
processing, as far as can be judged at present, is due to high bottom reflectivity, bottom
roughness and higher sound speed variability than during the ADVENT99 experiments
[1]. The large amount and quality of environmental data, which were collected together
with acoustics offers the opportunity to study the processes in detail, which are
responsible for coherence loss and signal degradation in a relatively benign littoral
ocean area.
Acknowledgements
The experiment was carried out as part of the SACLANTCEN Programme Of Work.
We thank crews and cruise participants for their outstanding dedication to the task.
FWG sponsored a considerable part of data processing including the development of
processing tools.
References
1. M. Siderius, P.L. Nielsen, J. Sellschopp, M. Snellen and D. Simons, Experimental study of
geo-acoustic inversion uncertainty due to ocean sound-speed fluctuations, J. Acoust. Soc.
Am. 110(2), 769–781 (2001).
2. A.R. Robinson et al., http://www.deas.harvard.edu/~leslie/ASCOT01/ (2001).
3. J. Sellschopp, A towed CTD chain for two dimensional high resolution hydrography, Deep
Sea Research 44(1), 145–163 (1997).
4. P.L. Nielsen, M. Siderius and J. Sellschopp, Broadband acoustic signal variability in two
“typical” shallow-water regions. In Impact of Littoral Environmental Variability on
Acoustic Predictions and Sonar Performance, edited by N.G. Pace and F.B. Jensen
(Kluwer, The Netherlands, 2002) pp. 237–244.
5. R. Evans, Program WAVE, http://oalib.saic.com/Other/wave/wave.zip (2001).
6. M. Siderius, P. Nielsen and J. Sellschopp, Source localization in a highly variable shallow
water environment: Results from ASCOT-01. In Impact of Littoral Environmental
Variability on Acoustic Predictions and Sonar Performance, edited by N.G. Pace and
F.B. Jensen (Kluwer, The Netherlands, 2002) pp. 425–432.