Tests of models for high-frequency seafloor backscatter

Jackson, Darrell R.; Briggs, Kevin B.; Williams, Kevin L.; Richardson, Michael D.

doi:10.1109/48.544057

Cited by 93 publications

(62 citation statements)

References 32 publications

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“…The sigma_2 parameter is a fitted parameter indicative of sediment volume scattering chosen to adjust the level of predicted backscatter to the level of the measured intensity. This parameter is used in the model presented in APL-UW-TR-9407 in lieu of the sound speed and density variance and density correlation length parameters (Table 1) used in the perturbation theory approach to sediment volume scattering (Jackson et al, 1996). Mean values are slightly different from Table 1 because (1) we restricted the database exclusively to measurements that were co-located at each depth and (2) core length varied both within and among sites.…”

Section: Regression Analysismentioning

confidence: 99%

NRL-APL Grain Size Algorithm Upgrade

Briggs¹,

Jackson²,

Moravan³

2002

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Standard Form 298 (Rev. 8-98)Prescribed by ANSI Std. Z39.18Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Approved for public release; distribution unlimited. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) SPONSOR / MONITOR'S ACRONYM(S) 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR / MONITOR'S REPORT NUMBER(S)This upgrade was designed to improve upon older algorithms used to infer input geoacoustic parameters for high-frequency acoustic models from sediment grain size. These older algorithms were based on a limited data set and were developed by adjusting acoustic model-data fits rather than by statistical regression. The upgrade has two components. The regression analysis of sediment grain size and geoacoustic properties was performed by NRL and the determination of accuracy of acoustic backscatter predictions was performed by APL-UW. The geoacoustic properties/ sediment grain size relationships produced a new algorithm connecting acoustic model parameters with the parameters of the MIW (Mine Warfare) sediment database. UnclassifiedUnclassified Unclassified UL 38

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Section: Regression Analysismentioning

confidence: 99%

NRL-APL Grain Size Algorithm Upgrade

Briggs¹,

Jackson²,

Moravan³

2002

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“…These data, when corrected for seafloor slope, beam pattern, time varying and angle varying gains, and area of insonification provide an estimate of seafloor acoustic backscatter strength (Urick 1983). Theoretical models and experimental observations demonstrate that backscatter from the seafloor, for a given incidence angle, is a complex function of many factors, including signal frequency, seabed roughness, grain size distribution, benthos, bioturbation, presence of gas and volume reverberation (Jackson et al 1986(Jackson et al , 1996de Moustier and Alexandrou 1991;Lyons et al 1994;Williams et al 2002). Backscatter is also strongly dependant on incidence angle, with backscatter strength near nadir (i.e.…”

Section: Introductionmentioning

confidence: 97%

Insights into environmental drivers of acoustic angular response using a self-organising map and hierarchical clustering

et al. 2015

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Acoustic backscatter from the seafloor is a complex function of signal frequency, seabed roughness, grain size distribution, benthos, bioturbation, volume reverberation, and other factors. Angular response is the variation in acoustic backscatter with incident angle and is considered be an intrinsic property of the seabed. An unsupervised classification technique combining a self-organising map (SOM) and hierarchical clustering was used to create an angular response facies map and explore the relationships between acoustic facies and ground truth data. Cluster validation routines indicated that a two cluster solution was optimal and separated sediment dominated environments from mixtures of sediment and hard ground. Low cluster separation limited cluster validation routines from identifying fine cluster structure visible with an AR density plot. Cluster validation, aided by a visual comparison with an AR density plot, indicated that a 14 cluster solution was also a suitable representation of the input dataset. Clusters that were a mixture of hard and unconsolidated substrates displayed an increase in backscatter with an increase in the occurrence of hard ground and highlighted the sensitivity of AR curves to the presence of even modest amounts of hard ground. Remapping video observations and sediment data onto the SOM matrix is innovative and depicts the relationship between ground truth data and cluster structure. Mapping environmental variables onto the SOM matrix can show broad trends and localised peaks and troughs and display the variability of ground truth data within designated clusters. These variables, when linked to AR curves via clusters, can indicate how environmental factors influence the shape of the curves. Once these links are established they can be incorporated into improved geoacoustic models that replicate field observations.

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“…Rough surface scattering will not be included here but can be added in a straightforward manner. While the necessity to include poro-elastic effects in a sediment scattering model remains an open question [18], a fluid model is appropriate for the soft sediments and low frequencies under consideration. The volume inhomogeneities, i. e., the sound speed and density variations, are generated by the spectral method [19].…”

Section: Objective and Approachmentioning

confidence: 99%

“…The geoacoustic parameters in Fig. 2-10 are from the CBBL Panama City site [18]. Frequency of 40kHz is chosen, the same as that in the experiment.…”

Section: ~-40mentioning

confidence: 99%

“…We will concentrate on propagation models in this study, which is equivalent to evaluating the Green's function between t he source/receiver and the scatterer. The necessity to include pore-elastic effects in t he scattering model remains an open question [18], and we will regard the bottom as an acoustic fluid. The free-space Green's function, together with the transmission coefficient at the water/bottom interface has been used in some work [24,53].…”

Section: Statement Of the Problemmentioning

confidence: 99%

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Modeling of monostatic bottom backscattering from three-dimensional volume inhomogeneities and comparisons with experimental data

Li¹

1997

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Acoustic propagation in the ocean inevitably encounters inhomogeneities of various types, which give rise to scattering. Acoustic scattering from rough water /bottom interfaces comprised of exposed rocks and sea mountains gives way to volumetric scattering in areas with fiat interfaces and thick sediment cover. The data analysis of the ARSRP backscattering experiment revealed that random inhomogeneities in two irregular layers beneath the seafloor were the primary contributors to oblique backscattering in a sediment pond on the western flank of the Mid-Atlantic Ridge. In this thesis, an attempt has been made to model monostatic backscattering from 3-D volume inhomogeneities in the sediment and to compare the results with the ARSRP backscattering data.A scattering process cannot be modeled correctly without a proper account of the incident field . Several approximate propagation models have been evaluated against the exact solution, while the appropriateness of using the equivalent surface scattering strength in volume scattering characterizations is studied. This study concludes that precautions need to be taken in modeling both the propagation effects and the scattering mechanisms associated with the bottom volume scattering process.A volume scattering model based on perturbation theory and the Born approximation is developed incorporating contributions from both sound speed and density fluctuations. With the propagation part handled accurately by OASES and random fluctuations generated effectively by a new scheme modified from the spectral method, the model is capable of simulating the monostatic backscattered field and time series due to 3-D volumetric sediment inhomogeneities. Both the characteristic length scale and power spectrum descriptions of the random inhomogeneities are shown to have great impact on the backscattered field by parameter studies in a free-space scenario. The important roles played by horizontal anisotropy and the vertical correlation of the random field have been demonstrated. Density fluctuations are further confirmed to be the dominant force in backscattering. The model matches the ARSRP backscattering data very well , with the fluctuations of sound speed and density in the two irregular layers described by a power law type of power spectrum. To my fellow-students in 5-007 and 5-435, thank you for t he wonderful atmosphere that you have created and t he team-work spirit t hat I have enjoyed. Believe me, you are t he best.Life would not have been so fun without all my friends. Qing, Henry, and Chenyang, how about playing some Tetris the next t ime we get together? And t hank you for introducing me t o the "real world" . Wenjie and Co. , guess no card game is scheduled for now. Thank you, Elaine, for many "Sloanish" advice, Lia ng-wu, for the ride to chase t he sunset, Richard , for the parties, and Feng, for making me a part-time Ashdowner. Helen, Lan and Xiaoou, I will surely not forget t he summer at Cape Cod.My most grateful thanks goes to my parents and my sister. You are ...

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Tests of models for high-frequency seafloor backscatter

Cited by 93 publications

References 32 publications

NRL-APL Grain Size Algorithm Upgrade

NRL-APL Grain Size Algorithm Upgrade

Insights into environmental drivers of acoustic angular response using a self-organising map and hierarchical clustering

Modeling of monostatic bottom backscattering from three-dimensional volume inhomogeneities and comparisons with experimental data

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