ARE CURRENT ENVIRONMENTAL DATABASES
ADEQUATE FOR SONAR PREDICTIONS
IN SHALLOW WATER?
CARLO M. FERLA AND FINN B. JENSEN
SACLANT Undersea Research Centre, Viale San Bartolomeo 400, 19138 La Spezia, Italy
E-mail: ferla@saclantc.nato.int, jensen@saclantc.nato.int
The usefulness of environmental databases (bathymetry, sound-speed profile and bottom reflectivity) for sonar performance predictions in high-variability littoral waters has
often been questioned. Thus it is conceivable that spatial and temporal averaging of
sparsely sampled data could result in data holdings which do not capture some of the
acoustically important environmental features required for accurate sonar predictions.
To address this issue on a larger geographical scale, SACLANTCEN has undertaken
a study using the Allied Environmental Support System (AESS) as the prediction tool
and the NATO Standard Oceanographic Data Base (NSODB) as the environmental representation. To quantify prediction errors in selected shallow-water areas as a function
of bottom type, water depth, season, frequency, sonar/target depth, etc., SACLANTCEN’s vast broadband transmission-loss database established over the past 30 years
will be used as ground truth. Initial results from the Mediterranean and the Norwegian
Sea indicate that databank-based performance predictions in shallow water are indeed
unreliable and that the weakest link is the bottom-loss information.
1 Introduction
Oceanographic and geophysical data collection programs have been operating for many
years with the scope of establishing reliable data sources for sonar predictions on a global
scale. Of course, the quality and the temporal and spatial coverage of the data vary
significantly from area to area, with the Mediterranean maybe being one of the best
mapped seas of the world. In using a gridded database, one has generally no information
about the number of original data points and their spatial distribution nor about the
measurement accuracy (some data values are derived values based on measuments with
different types of instruments). However, despite the uneven data coverage and quality,
when no in situ measurements are available, environmental database (bathymetry, soundspeed profiles, bottom reflectivity) are routinely used to forecast the sonar range of the
day for operating navies. Of course, sonar operators know perfectly well that these
databank-based range predictions are not always accurate.
Historically, much of the validation work associated with performance predictions has
been carried out in deep water, which was the main operating theater during the Cold
War. In deep water the sound speed structure is very stable below a few hundred meters,
both temporally and spatially. Also the upper ocean shows less variability than in coastal
areas. Moreover, important acoustic paths do not interact with the bottom in deep oceans,
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N.G. Pace and F.B. Jensen (eds.), Impact of Littoral Environmental Variability on Acoustic Predictions and
Sonar Performance, 555-562.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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and, hence, neither the bathymetry nor the bottom reflectivity are critical parameters for
sonar performance predictions. The result is that reliable sonar range predictions can
indeed be performed in deep water with current databases.
In the past decade the navy operational interest has shifted heavily towards littoral
waters, and here both spatial and temporal variability is a limiting factor for database
usage. Moreover, important acoustic paths in shallow water all interact with the bottom and, hence, both bathymetry and bottom reflectivity become critical parameters for
sonar predictions. SACLANTCEN has pioneered the study of shallow-water acoustics,
both experimentally and theoretically, and some of the key issues related to accurate
transmission-loss (TL) predictions were reported at conferences dating back to the early
1980s [1, 2].
That current “deep water” databases are inadequate for performance predictions in
shallow water is generally accepted, but there has been no attempt to quantify prediction
errors on an area-by-area basis and as a function of bottom type, water depth, season,
frequency, sonar/target depth, etc. SACLANTCEN has developed a strategy for doing
exactly this, which involves using the Centre’s vast broadband transmission-loss database
established over the past 30 years as ground truth, to which AESS/NSODB predictions
will be compared. Detailed analysis will be performed with high-fidelity models from the
Centre’s model library [3]. Initial results are presented from areas of the Mediterranean
and the Norwegian Sea.
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Figure 1. Environment for Strait of Sicily experiment.
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PAREQ_HIFI
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Figure 2. Model-data comparisons at 630 Hz for (a) the north-going track and (b) the south-going
track.
2
Strait of Sicily
This data set was collected in September 1996 as part of a major oceanographic/acoustic
survey of the Malta Plateau. The measured environmental conditions are given in Fig. 1,
indicating that the experiment was carried out with a receiver array suspended in the water
column, and with a source ship dropping SUS charges along two 35-km tracks, one to
the north into shallower water, and one to the south in almost constant water depth of
100 m. Both the measured bathymetry and the range-smoothed version used as input to
the acoustic models are shown in Fig. 1(a). The recorded sound-speed profiles along the
tracks during the experiments are shown in Fig. 1(b). Note that the water is warmer and
more stable to the south. The highest variability is observed when moving into shallower
water on the north-going track. In the acoustic inversion to determine average geoacoustic
properties for each track, a single representive profile was selected from each group of
profiles shown in Fig. 1(b).
Figure 2 shows model-data comparisons for a frequency of 630 Hz and for a source at
50 m and a receiver at 18 m. The upper graph is for the northern track into shallower water,
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AESS
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Figure 3. Model-data comparisons at 3.2 kHz for (a) the north-going track and (b) the south-going
track.
whereas the lower graph is for the track to the south. The first thing to note in both graphs
is the excellent agreement between the high-fidelity model results (PAREQ HIFI) [4] and
the data, which, in turn, provides a measure of confidence in the quality of the data. The
kind of agreement seen here was obtained for all hydrophone depths and for frequencies
between 100 and 3200 Hz (see Fig. 3 for results at 3.2 kHz). The geoacoustic models
derived from the inversions were similar on the two tracks: a 3-m soft top layer with
a 1.5% lower sound speed than in the water column near the bottom (c ≈ 1482 m/s), a
density of 1.5 g/cm3 and an attenuation of 0.1 dB/λ to the north and 0.15 dB/λ to the south.
The subbottom was found to have the following properties: c = 1650 m/s, ρ = 1.9 g/cm3 ,
α = 0.5 dB/λ.
The next step was to obtain AESS predictions based solely on database information.
Hence the exact track coordinates were provided and a TL prediction obtained from the
ASTRAL model, which has been determined to be the most reliable among the various
acoustic models available to the AESS user. The AESS predictions (red curves) in Fig. 2
generally provide too little loss and hence too long sonar ranges. The rapid fall-off
beyond 20 km in the upper graph is due to wrong bathymetry values in the database for
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ENVIRONMENTAL DATABASES AND SONAR PREDICTIONS
the shallow end of the north-going track.
To determine which database input from NSODB is primarily causing the prediction
error seen here, we designed a control case with the GRAB model [5], which can run
inputs directly from the NSODB. First GRAB was run with the same high-fidelity data
as PAREQ, and Fig. 2 shows that we obtain consistent answers in good agreement with
the data. Next we take just the seasonal mean sound-speed profile from the NSODB,
which only changes the GRAB prediction slightly. Similarly, if we use the bathymetry
information form the NSODB, we get only slight changes, except beyond 20 km on the
northern track, where the database values are much too shallow (< 20 m). Finally, if
GRAB is run with the bottom-loss information retrieved from NSODB we obtain the
results given by the dashed curves in Figs. 2(a) and (b) (GRAB LFBL). Here LFBL
refers to the low-frequency bottom-loss tables to be applied below 1 kHz. It is clear that
the bottom-loss model is responsible for the optimistic prediction ranges obtained with
the AESS.
Moving now to the 3.2-kHz results in Fig. 3, we note the excellent agreement between
data and the high-fidelity model predictions (PAREQ HIFI), which are based on the exact
same geoacoustic model used for the low-frequency case. The AESS prediction using the
high-frequency bottom-loss tables in NSODB provides too high losses and hence too short
sonar ranges. The red curve is clearly truncated at a maximum loss of around 105 dB. By
running GRAB in a control mode, it is easily shown that the higher losses predicted by
the AESS is caused by the bottom-loss model used. Again there is little effect of using
database information for sound-speed profile and bathymetry.
In terms of performance predictions in this particular area of the Mediterranean, it is
clear that the bottom-loss information is the weakest point of the database. Thus, there
is too little bottom loss at low frequencies and too much bottom loss at high frequencies.
This, in turn, means that the sonar range predictions are discontinuous around 1 kHz.
In practice, the transition is smoothed over a 500 Hz band, but we could still see level
differences of tens of decibels by changing the frequency from 1.0 to 1.5 kHz. A single
geoacoustic model as used in the HiFi modeling avoids such artifacts.
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Figure 4. Environment for Norwegian Shelf experiment.
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Figure 5. Model-data comparison at 400 and 2000 Hz for a deep source and shallow receiver.
3
Norwegian Shelf
The second data set was collected along the Norwegian west coast in September 1993,
again using SUS charges and a vertical hydrophone array for reception. Figure 4 shows
a cross-section of the acoustic track which runs parallel to the shelf break in 180 meters
of water. There are 8 measured sound-speed profiles with an average spacing of 15 km.
There is some variability in the oceanographic conditions along the track, but there is
always a well-defined mixed layer (20–30 m deep) followed by a sharp thermocline.
All eight profiles were used in the geoacoustic inversion, whereas the water depth was
considered constant along the entire track.
Figure 5 shows model-data comparisons at 400 and 2000 Hz for a source at 150 m and
a receiver at 18 m. Note the excellent agreement between the high-fidelity model results
(C-SNAP HIFI) [6] and the experimental data. This kind of agreement was observed for
all receiver depths and for frequencies between 50 and 2000 Hz, which lends credence to
both the quality of the data and the geoacoustic model. A simple homogeneous bottom
with c = 1670 m/s, ρ = 2.0 g/cm3 and α = 0.5 dB/λ was found to adequately represent
bottom reflectivity for the entire frequency band.
To assess the quality of AESS-based sonar predictions for this area, we provided track
coordinates, source/receiver geometry, frequency and season (September) to the AESS
system and requested a TL prediction with the ASTRAL model. The result is shown in
Fig. 5 (red curves) and there is clearly too much bottom loss at both frequencies. We
have yet to run the control cases with the GRAB model to determine whether the profile
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Loss (dB)
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Figure 6. Model-data comparison at 400 and 2000 Hz for a mid-water source and receiver.
and bathymetry information in the NSODB is adequate.
Turning to the case of a mid-water source and receiver (Fig. 6) there is an indication
that it is not just the bottom-loss information that is inaccurate in the NSODB. Thus for
source and receiver both at 90 m, we should expect excellent propagation conditions with
sound being channeled to long distances with little bottom interaction. The AESS result
at 400 Hz has the correct shape, but the red curve is displaced down by 10 dB compared
to the data. This type of problem is most likely associated with the use of an incorrect
sound-speed profile. These issues will all be investigated by running control cases with
GRAB for each of the NSODB inputs, i.e. sound-speed profile, bathymetry and bottom
reflectivity.
In summarizing the results in Figs. 5 and 6 for the Norwegian Shelf, we note that the
AESS always predicts too much loss and hence too short sonar ranges. This behavior is
quite different from the Strait of Sicily predictions in Sect. 2, where the low-frequency
results showed too little loss, and this despite the fact that the two geoacoustic environments were found to be very similar. Clearly, the effects of inaccurate data in the NSODB
on sonar performance predictions can have many different manifestations, and many data
sets must be analysed in order to create a statistically significant decision basis for determining the geographical areas and operational situations for which current databases are
inadequate for sonar performance predictions.
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Conclusions
In trying to address the question whether current environmental databases (NSODB) are
adequate for sonar performance predictions in shallow water, we have looked at two
different geographical areas (the Strait of Sicily and the Norwegian Shelf) and compared
three different views of the same acoustic picture:
1. High-quality broadband transmission-loss data representing ground truth.
2. High-fidelity TL predictions based on in situ oceanographic inputs and inverted
geoacoustic information.
3. AESS generated TL predictions based on environmental information from the
NSODB.
The picture that emerges from this comparison is rather complex, but the AESS prediction
is generally deemed unsatisfactory, due mainly to inaccurate environmental inputs from
the NSODB. The bottom-loss information is found to be the weakest link, but also
bathymetry and profile information can have adverse effects on the prediction accuracy.
More areas and more acoustic track need to be analysed before a general statement can be
made about the usefulness of the NSODB for performance predictions in littoral waters.
References
1. Kuperman, W.A. and Jensen, F.B. (eds.), Bottom-Interacting Ocean Acoustics (Plenum Press,
New York, 1980).
2. Akal, T. and Berkson, J.M. (eds.), Ocean Seismo-Acoustics (Plenum Press, New York, 1986).
3. Jensen, F.B., Ferla, C.M., LePage, K.D. and Nielsen, P.L., Acoustic models at SACLANTCEN: An update. Report SR-354, SACLANT Undersea Research Centre, La Spezia, Italy
(2001).
4. Jensen, F.B. and Martinelli, M.G., The SACLANTCEN parabolic equation model (PAREQ).
SACLANT Undersea Research Centre, La Spezia, Italy (1985).
5. Weinberg, H. and Keenan, R.E., Gaussian ray bundles for modeling high-frequency propagation loss under shallow-water conditions, J. Acoust. Soc. Am. 100, 1421–1431 (1996).
6. Ferla, C.M., Porter, M.B. and Jensen, F.B., C-SNAP: Coupled SACLANTCEN normal mode
acoustic propagation loss model. Report SM-274, SACLANT Undersea Research Centre,
La Spezia, Italy (1994).