Time-evolving acoustic propagation modeling in a complex ocean environment

Abstract

Abstract-During naval operations, sonar performance estimates often need to be computed in-situ with limited environmental information. This calls for the use of fast acoustic propagation models. Many naval operations are carried out in challenging and dynamic environments. This makes acoustic propagation and sonar performance behavior particularly complex and variable, and complicates prediction. Using data from a field experiment, we have investigated the accuracy with which acoustic propagation loss (PL) can be predicted, using only limited modeling capabilities. Environmental input parameters came from various sources that may be available in a typical naval operation.The outer continental shelf shallow-water experimental area featured internal tides, packets of nonlinear internal waves, and a meandering water mass front. For a moored source/receiver pair separated by 19.6 km, the acoustic propagation loss for 800 Hz pulses was computed using the peak amplitude. The variations in sound speed translated into considerable PL variability of order 15 dB. Acoustic loss modeling was carried out using a data-driven regional ocean model as well as measured sound speed profile data for comparison. The acoustic model used a two-dimensional parabolic approximation (vertical and radial outward wavenumbers only). The variance of modeled propagation loss was less than that measured. The effect of the internal tides and sub-tidal features was reasonably well modeled; these made use of measured sound speed data. The effects of nonlinear waves were not well modeled, consistent with their known three-dimensional effects but also with the lack of measurements to initialize and constrain them.

A Coupled-Mode Shallow-Water Model for Tidal Analysis: Internal Tide Reflection and Refraction by the Gulf Stream

“…During these experiments, numerous oceanographic and acoustic measurements were collected from July through September 2006 (Newhall et al 2007;Lynch and Tang 2008;Colosi et al 2012), and primitive equation forecasts with data assimilation, reanalyses, and adaptive sampling recommendations were issued in real time using the Multidisciplinary Simulation, Estimation, and Assimilation System (MSEAS; Lermusiaux et al 2006;Haley and Lermusiaux 2010;Lin et al 2010;Colin et al 2013). Afterward, more than 1400, implicit, two-way-nested primitive equation reanalyses were completed to improve all aspects of the field estimation.…”

Modeling and Forecasting Ocean Acoustic Conditions

“…These studies show slices through 4D-variable sound-speed structures obtained from a data-driven ocean model, plus line drawings and histograms of sound level computed at points and along transects. Another example (Colin et al 2013) also models sound using conditions obtained from an ocean model, and concentrates on time-dependence of the sound field along a single acoustic propagation path. Lermusiaux et al (2010) and Colin et al (2013) use the most recent technology, simulating sound through environments computed with data-driven (data assimilating) ocean dynamical models (with one exception: they do not use 3D acoustic modeling).…”

“…Another example (Colin et al 2013) also models sound using conditions obtained from an ocean model, and concentrates on time-dependence of the sound field along a single acoustic propagation path. Lermusiaux et al (2010) and Colin et al (2013) use the most recent technology, simulating sound through environments computed with data-driven (data assimilating) ocean dynamical models (with one exception: they do not use 3D acoustic modeling). Stepping [75,3 back from this level of complexity, which requires good data in four dimensions, other methods allow rudimentary propagation simulation and prediction.…”

“…This subject is often analyzed in terms of transmission loss (TL), measured in decibels relative to the source level. Example papers, chosen from hundreds, are Ngheim-Phu and Tappert (1985), Finette et al (2007), and the more recent Colin et al (2013). The technique of modeling noise (sound from distributed sources) can be done by adding fields from many sources (Kuperman and Ingenito 1980;Udovydchenkov et al 2010), Together, these enable signal-to-noise ratio modeling and via the spatial coherences of the modeled signal and noise, they enable modeling of coherent array processing gain (Finette and Oba 2003;Real et al 2015).…”

Autonomous and adaptive oceanographic feature tracking on board autonomous underwater vehicles

“…Other MSEAS subsystems include: initialization schemes [101], nested data-assimilative tidal prediction and inversion [102]; fast-marching coastal objective analysis [103]; stochastic subgrid-scale models (e.g., [104,105]); generalized adaptable biogeochemical modeling systems; Lagrangian Coherent Structures; non-Gaussian data assimilation and adaptive sampling [106][107][108]; dynamically-orthogonal equations for uncertainty predictions [109][110][111]; and machine learning of model formulations [112]. e MSEAS software is used for basic and fundamental research and for realistic simulations and predictions in varied regions of the world's ocean [113][114][115][116][117][118][119][120], including monitoring [121], naval exercises including real-time acoustic-ocean predictions [122] and environmental management [123]. …”