Journal of the Acoustical Society of America, 2016
The goal of timely and accurate acoustics modeling in the ocean depends on accurate environmental... more The goal of timely and accurate acoustics modeling in the ocean depends on accurate environmental input information. Acoustic propagation modeling has improved to the point of possibly being ahead of ocean dynamical modeling from the standpoint that some significant ocean features having strong acoustic effects are not faithfully reproduced in many models, particularly data-driven ocean models. This in part stems from the fact that ocean models have developed with other goals in mind, but computational limitations also play a role. The Integrated Ocean Dynamics and Acoustics (IODA) MURI project has as its goals improving ocean models, and also making continued improvements to acoustic models, for the purpose of advancing ocean acoustic modeling and prediction capabilities. Two major focuses are improved internal tide forecasting and improved nonlinear internal wave forecasting, which require pushing the state of the art in data-constrained mesoscale feature modeling as well as devel...
Journal of the Acoustical Society of America, Oct 1, 2022
In marine applications, the value of accurate modeling and learning for stochastic acoustic propa... more In marine applications, the value of accurate modeling and learning for stochastic acoustic propagation in uncertain ocean environments cannot be overstated. In this work, we derive stochastic theory and schemes for (i) modeling of high frequency acoustic propagation in uncertain ocean environments and (ii) joint Bayesian assimilation of ocean-acoustic measurements to infer fields, parameters, and uncertain model functions. We first obtain the Dynamically Orthogonal (DO) wavefront equations to solve for the stochastic extension of the Liouville Equation that governs the dynamics of acoustic wavefront in an augmented phase space. These DO wavefront equations provide the prior for the Gaussian Mixture Model—DO (GMM-DO) filter that completes joint physics-acoustics Bayesian inference using sparse observations. Specifically, given a set of receivers, the Eulerian nature of the DO wavefront equations allows for the efficient extraction of arrival time prior probability distributions. The GMM-DO Waverfront filter then combines these joint priors with arrival time measurements using Bayes rule, jointly inferring environmental properties (e.g., unknown source location and/or sound speed field), the acoustic wavefront distribution, and the arrival time distribution itself. We evaluate results using high-frequency applications, illustrating the estimation of mean fields and properties, but also of probability density distributions and model parameterizations.
Journal of the Acoustical Society of America, Oct 1, 2022
New findings in outer-shelf and shelfbreak acoustics have been enabled by experimental and comput... more New findings in outer-shelf and shelfbreak acoustics have been enabled by experimental and computational advances over the last 25 years. The details of sound field variability caused by highly dynamic conditions often found in this regime have become measurable through advances in data collection technology. Furthermore, these details can now be computationally modeled more realistically. The coupling of more plentiful data and higher fidelity modeling has uncovered many new behaviors. It has also allowed us to quantify the effects on sound level and phase structure (coherence) of many outer-shelf physical features, as well as the temporal aspects of these variations. Key tools have been vessel dynamic positioning, underwater position finding, small mobile platforms, high-capacity multichannel receive arrays, data-assimilating regional ocean dynamical models, nonlinear wave modeling, and three-dimensional acoustic propagation modeling. Examples from the published work of the authors, and the work of others, of how these advances have fostered new knowledge of specific processes will be presented, as well as present-day challenges inspired by recent findings.
Journal of the Acoustical Society of America, Oct 1, 2021
Accurate modeling of underwater acoustic propagation is challenging due to the complex ocean phys... more Accurate modeling of underwater acoustic propagation is challenging due to the complex ocean physics and acoustic dynamics and the need for resolving the wavelength of the propagating acoustic wave over large distances. These challenges are further amplified by the incomplete knowledge of the ocean environment and the acoustic parameters. These complexities thus lead to many sources of uncertainties in the governing models. In this work, we use our stochastic Dynamically Orthogonal (DO) framework to represent these uncertainties probabilistically in the acoustic Parabolic Equation (PE). These equations optimally represent the dominant uncertainties in the sound speed, density, bathymetry, and acoustic pressure fields. Starting from the governing PE, we derive range-evolution DO differential equations for the mean field, stochastic modes, and coefficients, hence preserving the nonlinearities and capturing the non-Gaussian statistics. The DO equations are implemented for the narrow-angle PE and higher-order Padé wide-angle PEs and are applied in range-dependent canonical test cases and realistic ocean environments with uncertain source location, source frequency, sound speed, and/or bathymetry fields. We highlight the computational advantages of our framework by comparing it to Monte Carlo predictions and show convergence of the probability density functions as the number of samples and/or modes is increased.
Journal of the Acoustical Society of America, 2016
We discuss the prediction and estimation of multiscale ocean fields and their probability density... more We discuss the prediction and estimation of multiscale ocean fields and their probability density distribution for acoustic studies. In high-fidelity multi-resolution simulations, the probability density function of the full ocean state is predicted and estimated, combining the governing equations with observations. Dynamically balanced stochastic forcing are included, so as to represent effects of sub-grid-scales not resolved by the deterministic model equations. The results are stochastic partial differential equations that allow to capture both deterministic effects (advection, Coriolis, etc.) and statistical effects (smaller-scale turbulence, internal wave variability, etc.) on the environment. With this modeling and data assimilation, accurate estimates of the probability density functions (pdf) of oceanographic variability are possible. They become inputs to end-to-end oceanographic-seabed-acoustic-sonar dynamical systems. This end-to-end uncertainty quantification approach is described, evaluated, and illustrated in several simulations and ocean regions.We discuss the prediction and estimation of multiscale ocean fields and their probability density distribution for acoustic studies. In high-fidelity multi-resolution simulations, the probability density function of the full ocean state is predicted and estimated, combining the governing equations with observations. Dynamically balanced stochastic forcing are included, so as to represent effects of sub-grid-scales not resolved by the deterministic model equations. The results are stochastic partial differential equations that allow to capture both deterministic effects (advection, Coriolis, etc.) and statistical effects (smaller-scale turbulence, internal wave variability, etc.) on the environment. With this modeling and data assimilation, accurate estimates of the probability density functions (pdf) of oceanographic variability are possible. They become inputs to end-to-end oceanographic-seabed-acoustic-sonar dynamical systems. This end-to-end uncertainty quantification approach is described, evaluated, ...
Journal of the Acoustical Society of America, 2016
The goal of timely and accurate acoustics modeling in the ocean depends on accurate environmental... more The goal of timely and accurate acoustics modeling in the ocean depends on accurate environmental input information. Acoustic propagation modeling has improved to the point of possibly being ahead of ocean dynamical modeling from the standpoint that some significant ocean features having strong acoustic effects are not faithfully reproduced in many models, particularly data-driven ocean models. This in part stems from the fact that ocean models have developed with other goals in mind, but computational limitations also play a role. The Integrated Ocean Dynamics and Acoustics (IODA) MURI project has as its goals improving ocean models, and also making continued improvements to acoustic models, for the purpose of advancing ocean acoustic modeling and prediction capabilities. Two major focuses are improved internal tide forecasting and improved nonlinear internal wave forecasting, which require pushing the state of the art in data-constrained mesoscale feature modeling as well as devel...
Journal of the Acoustical Society of America, Oct 1, 2022
In marine applications, the value of accurate modeling and learning for stochastic acoustic propa... more In marine applications, the value of accurate modeling and learning for stochastic acoustic propagation in uncertain ocean environments cannot be overstated. In this work, we derive stochastic theory and schemes for (i) modeling of high frequency acoustic propagation in uncertain ocean environments and (ii) joint Bayesian assimilation of ocean-acoustic measurements to infer fields, parameters, and uncertain model functions. We first obtain the Dynamically Orthogonal (DO) wavefront equations to solve for the stochastic extension of the Liouville Equation that governs the dynamics of acoustic wavefront in an augmented phase space. These DO wavefront equations provide the prior for the Gaussian Mixture Model—DO (GMM-DO) filter that completes joint physics-acoustics Bayesian inference using sparse observations. Specifically, given a set of receivers, the Eulerian nature of the DO wavefront equations allows for the efficient extraction of arrival time prior probability distributions. The GMM-DO Waverfront filter then combines these joint priors with arrival time measurements using Bayes rule, jointly inferring environmental properties (e.g., unknown source location and/or sound speed field), the acoustic wavefront distribution, and the arrival time distribution itself. We evaluate results using high-frequency applications, illustrating the estimation of mean fields and properties, but also of probability density distributions and model parameterizations.
Journal of the Acoustical Society of America, Oct 1, 2022
New findings in outer-shelf and shelfbreak acoustics have been enabled by experimental and comput... more New findings in outer-shelf and shelfbreak acoustics have been enabled by experimental and computational advances over the last 25 years. The details of sound field variability caused by highly dynamic conditions often found in this regime have become measurable through advances in data collection technology. Furthermore, these details can now be computationally modeled more realistically. The coupling of more plentiful data and higher fidelity modeling has uncovered many new behaviors. It has also allowed us to quantify the effects on sound level and phase structure (coherence) of many outer-shelf physical features, as well as the temporal aspects of these variations. Key tools have been vessel dynamic positioning, underwater position finding, small mobile platforms, high-capacity multichannel receive arrays, data-assimilating regional ocean dynamical models, nonlinear wave modeling, and three-dimensional acoustic propagation modeling. Examples from the published work of the authors, and the work of others, of how these advances have fostered new knowledge of specific processes will be presented, as well as present-day challenges inspired by recent findings.
Journal of the Acoustical Society of America, Oct 1, 2021
Accurate modeling of underwater acoustic propagation is challenging due to the complex ocean phys... more Accurate modeling of underwater acoustic propagation is challenging due to the complex ocean physics and acoustic dynamics and the need for resolving the wavelength of the propagating acoustic wave over large distances. These challenges are further amplified by the incomplete knowledge of the ocean environment and the acoustic parameters. These complexities thus lead to many sources of uncertainties in the governing models. In this work, we use our stochastic Dynamically Orthogonal (DO) framework to represent these uncertainties probabilistically in the acoustic Parabolic Equation (PE). These equations optimally represent the dominant uncertainties in the sound speed, density, bathymetry, and acoustic pressure fields. Starting from the governing PE, we derive range-evolution DO differential equations for the mean field, stochastic modes, and coefficients, hence preserving the nonlinearities and capturing the non-Gaussian statistics. The DO equations are implemented for the narrow-angle PE and higher-order Padé wide-angle PEs and are applied in range-dependent canonical test cases and realistic ocean environments with uncertain source location, source frequency, sound speed, and/or bathymetry fields. We highlight the computational advantages of our framework by comparing it to Monte Carlo predictions and show convergence of the probability density functions as the number of samples and/or modes is increased.
Journal of the Acoustical Society of America, 2016
We discuss the prediction and estimation of multiscale ocean fields and their probability density... more We discuss the prediction and estimation of multiscale ocean fields and their probability density distribution for acoustic studies. In high-fidelity multi-resolution simulations, the probability density function of the full ocean state is predicted and estimated, combining the governing equations with observations. Dynamically balanced stochastic forcing are included, so as to represent effects of sub-grid-scales not resolved by the deterministic model equations. The results are stochastic partial differential equations that allow to capture both deterministic effects (advection, Coriolis, etc.) and statistical effects (smaller-scale turbulence, internal wave variability, etc.) on the environment. With this modeling and data assimilation, accurate estimates of the probability density functions (pdf) of oceanographic variability are possible. They become inputs to end-to-end oceanographic-seabed-acoustic-sonar dynamical systems. This end-to-end uncertainty quantification approach is described, evaluated, and illustrated in several simulations and ocean regions.We discuss the prediction and estimation of multiscale ocean fields and their probability density distribution for acoustic studies. In high-fidelity multi-resolution simulations, the probability density function of the full ocean state is predicted and estimated, combining the governing equations with observations. Dynamically balanced stochastic forcing are included, so as to represent effects of sub-grid-scales not resolved by the deterministic model equations. The results are stochastic partial differential equations that allow to capture both deterministic effects (advection, Coriolis, etc.) and statistical effects (smaller-scale turbulence, internal wave variability, etc.) on the environment. With this modeling and data assimilation, accurate estimates of the probability density functions (pdf) of oceanographic variability are possible. They become inputs to end-to-end oceanographic-seabed-acoustic-sonar dynamical systems. This end-to-end uncertainty quantification approach is described, evaluated, ...
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