THE ROLE OF NOWCAST AND FORECAST INPUT
PARAMETERS FOR RANGE DEPENDENT
TRANSMISSION MODELS
JANICE S. SENDT
Thales Underwater Systems Pty, 274 Victoria Road, Rydalmere NSW 2116, Australia
E-mail: [email protected]
ADRIAN D. JONES AND JARRAD R. EXELBY
Defence Science and Technology Organisation, P.O.Box 1500, Edinburgh SA 5111, Australia
E-mail: [email protected]
The Maritime Operations Division of DSTO is assisting the Royal Australian Navy in its
assessment of a sonar performance prediction tool for range dependent ocean
environments: TESS 2, prepared by Thales Underwater Systems. This assessment has
included comparisons between acoustic transmission loss data measured by MOD at
shallow ocean sites with range-dependent transmission predictions obtained by TUS.
Part of this task has been the inference of the appropriate input parameters from the
measured data and comparison with historic data sets. This applies to bathymetry and to
seafloor acoustic properties. Examples detailed in this paper indicate the variability in
properties which must be described at differing regional locations and the difficulty that
can occur at arriving at suitable input parameters from historical data alone. These
examples include reference to high resolution bathymetry (ocean depth) data, and the
inference of seafloor reflectivity based on received signal data.
1
Introduction
This paper addresses some aspects of a joint DSTO/TUS Pty task for benchmarking the
range dependent acoustic transmission loss models used in the TESS 2 software. The
TESS 2 software provides performance prediction for the Royal Australian Navy (RAN)
platform sonar sensor systems. The part of this assessment which is addressed in this
paper has been the comparison of results from TESS 2 with measured results from a
considerable number of shallow water sites around Australia which had been collected
and analysed by DSTO. Also discussed briefly is an assessment of the potential for an
MOD in-situ technique to infer seafloor reflectivity at shallow grazing angles and provide
input to TESS 2 for regions for which existing holdings of seafloor properties are sparse.
The TESS 2 software, in particular, the underwater component called SAGE [1], was
developed by TUS Pty. SAGE allows the user to compute sonar performances in
detection range for realistic ocean environments located at a user defined latitude and
longitude. It achieves its purpose by accessing appropriate internal global databases and
supplying the necessary parameters to run range dependent sonar performance models.
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N.G. Pace and F.B. Jensen (eds.), Impact of Littoral Environmental Variability on Acoustic Predictions and
Sonar Performance, 547-554.
© 2002 All Rights Reserved. Printed in the Netherlands.
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The databases include bathymetry, wave height, wind speed, sound speed, sediment
thickness and a global sediment province database (TUS proprietary).
This paper addresses the approach taken in analyzing the input data from three
different sites. The data available from the measured results included start-of-track and
end-of-track core samples, a number of sound speed profiles along the track and echo
soundings at approximately every 1 km for most tracks.
The data available from the SAGE databases included gridded 30” bathymetry and
globally gridded 2’ sediment province information. The latter provides sediment
information along the whole length of the track whilst the core data only provides
information at two points on the track. Sound speed versus depth profiles determined
from site bathythermograph data were used in preference to the available historic data.
Additionally, a review of the geological work for the area was undertaken to assist in
understanding the biogenic and fine detail of the area.
At this point in time, this input data has been used only in the two SAGE
transmission loss models, namely RAM [2] which is a parabolic equation (PE) model
used for frequencies below about 400 Hz and RAVE which is a ray model (TUS
proprietary) used for higher frequencies. The examples shown in this paper, which refer
to the use of SAGE data within a TESS 2 framework, are from the RAM model.
2
Large bathymetry variations
At one site, a comparison of the bathymetry data showed an anomalous point where the
DSTO echo sounder data recorded a 30 m depth discrepancy (at 8 km along the track) to
the gridded data. The DSTO data around this point was more sparse than along the rest
of the track, with the previous point 6 km distant and next point 2 km distant from the
point in question (see Fig. 1). Agreement along the rest of the track was good. Thus the
echo sounder dataset was showing a gully possibly 8 km wide. The question arose as to
whether this point actually existed or was a misreading.
Depth, m
90
110
Echo sounder
30sec Bathy
130
150
0
5
10
15
20
25
Range, km
Figure 1. Bathymetry data at first site – echo sounder versus 30 second database.
INPUT PARAMETERS FOR TRANSMISSION MODELS
549
In turn, a doubt was raised as to whether the gridded data was in error. It was also
noted that the impact of this point on transmission loss was large (see Fig. 2). A review
of other data for this site showed that the track was crossing a relict river bed and that the
echo sounder point was indeed correct. However the agreement between the measured
and calculated results showed that the gridded bathymetry data gave the best match. The
transmission loss values calculated with the echo sounder data gave a discrepancy of up
to 10 dB at the ranges of the bathymetry discrepancy. The transmission loss discrepancy
continued at further ranges by producing an “out of phase” transmission loss pattern.
Figure 2. Comparison of measured transmission loss and values calculated using either gridded
bathymetry or echo sounder bathymetry with 8 km wide, 30 m deep gully centred 8 km from
source. Transmission data at 100 Hz for source and receiver at 18 m depth.
As the river channel does not appear in the gridded data set, its width may be
presumed less than 1 km. Accordingly, the width of the gully was reduced to 1 km and
the transmission loss re-calculated. Figure 3 shows that the transmission loss results for
the revised echo sounder data now show better agreement with the measured results.
Reducing the width further would allow the results to converge on the values obtained
with the gridded data set. If the measured dataset was larger, it would then be possible to
use the transmission loss model to infer the correct bathymetry profile, particularly as
there is only a one point discrepancy.
The existence of relict river beds is not an uncommon occurrence on the continental
shelf region. The results of this study emphasise the need for point bathymetry data to be
used in acoustic transmission loss modelling as well as gridded data. This data should be
provided as a “bedform” database and include the location, width and depth of relict river
beds.
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Figure 3. Comparison of measured transmission loss and values calculated using either gridded
bathymetry or modified echo sounder bathymetry with 1 km wide, 30 m deep gully centred 8 km
from source. Transmission data at 100 Hz for source and receiver at 18 m depth.
3
Biogenic effects
One of the difficulties in predicting transmission loss with reliability in Australian
northern waters is the biogenic impact. A grab sample may be analyzed into
gravel/sand/mud, based on mean grain size, but when this analysis also includes a calcium
content of 90% then the description is insufficient to discriminate between oolitic type
grain and the casts of marine mammals. If a porosity measurement has also been taken
then this will allow discrimination between the two to be quantified, and if this is not
available, then a marine geological description of the area can allow an estimate to be
made. Data obtained for a second site illustrates the difficulties which may be
encountered.
At the second site, the following data had been recorded across a 20 km track:
1. A surficial sediment grab sample at the start and end of the track.
2. 11 sound velocity profiles along the track.
3. Echo sounder depth data at approximately 300 m intervals (Fig. 4).
4. Transmission loss measurements at known ranges (Fig. 5).
Additional data which was available from the SAGE database:
5. The sediment province data for the track.
6. The calcium carbonate % for the sediment.
7. 30 arc second gridded bathymetry.
8. Sediment thickness data.
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INPUT PARAMETERS FOR TRANSMISSION MODELS
Depth, m
80
90
100
110
0
5
10
15
20
25
Range, m
Figure 4. Echo sounder bathymetry along the 20 km track, second site.
Figure 5. Comparison of predicted and measured transmission loss at 100 Hz for source and
receiver at 18 m depth.
The marine geological description for the area stated that there were irregularly
spaced sand waves. The high carbonate content of the sediment is due to the pellets of
eroded limestones and the relic skeletal debris.
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The core data describes the sediment as 95% sand and 95% calcite across the entire
track. If the core descriptors alone are used then the seabed would be reflective and the
transmission loss would decrease slowly with range. As this was not the case, in fact, at
the beginning of the track the transmission as measured is quite lossy (one-third octave
data is presented in Fig. 5), then the porosity of the sediment must be higher than would
be expected assuming a “standard sand”. It may be then be speculated that the calcium
carbonate content is due to the skeletal debris. It is also noted that for the deeper
bathymetry, further along the track, the seabed becomes more reflective indicative of a
reduction in the skeletal debris content of the sediment and an increase in the pellets.
As the transmission loss values at the start of the track indicate that the seabed is
acting as an absorbing layer, it is essential to get an estimate on the thickness of the
sediment to the acoustic basement as reflections from this layer may impact on the
transmission loss results. Shallow water seismic data is available for this area and
estimates from the two way travel time based on the speed of sound for the surficial
sediment give a sediment depth of 70 m [4]. This value is used as the sediment thickness
in the transmission calculation for the data presented in Fig. 5.
4
In-situ determination of seafloor reflectivity
In support of optimum sonar performance prediction capability, MOD has on-going
programmes of research on rapid environmental sensing techniques, with an emphasis on
the determination of effective seafloor parameters. In particular, MOD has developed a
unique method for in-situ determination of seafloor specular reflectivity [5]. The
potential value of this technique as an adjunct to the TESS 2 system is under present
investigation. Progress in this work is illustrated below.
To assess the potential for the MOD in-situ determination of seafloor reflectivity,
three-way comparisons have been carried out between: (i) measured transmission loss
data determined by MOD at specified ranges; (ii) predictions of transmission loss
obtained by the TESS 2 system; (iii) predictions of transmission loss obtained using
inverted seafloor reflectivity as input to the KRAKENC model [6]. The measured data
shown in this paper are obtained at a site near the shelf break, for which bathymetry along
two tracks is indicated by Fig. 6. Here, Run 5 was along a track which proceeded down
the continental slope, whereas Run 6 was an intersecting track which followed the
bathymetry contours, thereby retaining near uniform depth. The acoustic data capture
was afforded by deployment of sonobuoys and Mk 61 SUS from a P-3C maritime patrol
aircraft of the Royal Australian Air Force. For each track, the signal received from a
single SUS was input to the MOD algorithm and seafloor reflectivity determined for
shallow grazing angles.
The comparisons of transmission loss are shown in Figs. 7 and 8 respectively for
Run 6 (KRAKENC run range-independent) and Run 5 (KRAKENC run rangedependent). Here, the measured data was processed in one-third octave bands, the
KRAKENC data was obtained by coherent processing at frequencies within each onethird octave and then incoherently averaged, and the TESS 2 (RAM) prediction was
obtained coherently at a single frequency (250 Hz).
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INPUT PARAMETERS FOR TRANSMISSION MODELS
0
Run 5
Run 6
Depth (m)
-100
-200
-300
0
10
20
30
40
50
Range (km)
Figure 6. Bathymetry along Run 5 and Run 6 at 3rd site.
Transmission Loss (dB)
40
TESS 2
in-situ inversion
250 Hz measured
50
60
70
80
90
100
0
5
10
15
20
25
30
Range (km)
Figure 7. TL measured & predicted, Site 3 Run 6 – range-independent, 250 Hz.
As shown by the data in each of Figs. 7 and 8, the agreement between the TESS 2predicted and measured transmission loss values is very good for this site. This is
presumed due to the fact that considerable seafloor data exists in the historical record
from which the SAGE data was derived. Also at each site, the transmission data based on
the in-situ seabed reflectivity is close to the measured data.
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J.S. SENDT ET AL.
Transmission Loss (dB)
40
TESS 2
in-situ inversion
250 Hz measured
50
60
70
80
90
100
110
0
10
20
30
40
50
Range (km)
Figure 8. TL measured & predicted, Site 3 Run 5 – range-dependent, 250 Hz.
5
Conclusions
Based on the data presented in this paper, it does appear that operational models for the
prediction of sonar system performance may be best employed in a way in which collated
or gridded databases of historical information are supplemented, judiciously, by the input
of additional quality data for the local region. In shallow oceans, the spatial variability in
ocean depth and in seafloor properties requires range-dependent modelling which is
supplemented by local sampling when feasible.
Acknowledgements
The authors thank Mr. Paul Clarke for his assistance in the preparation of this paper.
References
1. TESS 2 operator’s manual, TUS (2000).
2. Collins, M.D., User’s guide for RAM versions 1.0 and 1.0p, available at:
[email protected]
3. Jones, H.A., Marine geology of the Northwest Australian Continental Shelf, Bulletin 136,
Bureau of Mineral Resources (1973).
4. Gravity, magnetic and seismic profiles of the Timor Sea, Australian Geological Survey
Organisation.
5. Jones, A.D., Bartel, D.W., Clarke, P.A. and Day, G.J., Acoustic inversion for seafloor
reflectivity in shallow water environment. In Proc. UDT Pacific 2000, Australia, 7–
9 February 2000.
6. Porter, M.B., The KRAKEN normal mode program. Rep. SM-245, SACLANT Undersea
Research Centre (1995).