Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsReasoning under Uncertainty: Stochastic OptimizationMachine Learning: Calibration & Uncertainty Quantification
A Physics-Guided Probabilistic Surrogate Modeling Framework for Digital Twins of Underwater Radiated Noise
🤖 AI Summary
Increasing maritime traffic along coastlines exacerbates underwater radiated noise, threatening marine ecosystems. Method: This paper proposes a physics-guided probabilistic surrogate model to construct an underwater noise digital twin for three-dimensional transmission loss (TL) prediction. It integrates a learnable physics-informed mean function, terrain- and coordinate-aware neural encoders, a residual stochastic process, sparse variational Gaussian processes, and deep sigma-point processes—trained on large-scale synthetic data generated by a spherical-expansion–frequency-dependent absorption physical model and a Gaussian beam solver, enabling uncertainty-calibrated, efficient acoustic field modeling. Contribution/Results: The approach achieves, for the first time, real-time, seasonally adaptive, broadband 3D TL prediction, supporting worst-case scenario analysis, exposure boundary modeling, and noise impact assessment. Validated in the Salish Sea, it successfully informed vessel speed optimization strategies, significantly reducing acoustic impacts on marine mammals.
Technology Category
Application Category
📝 Abstract
Ship traffic is an increasing source of underwater radiated noise in coastal waters, motivating real-time digital twins of ocean acoustics for operational noise mitigation. We present a physics-guided probabilistic framework to predict three-dimensional transmission loss in realistic ocean environments. As a case study, we consider the Salish Sea along shipping routes from the Pacific Ocean to the Port of Vancouver. A dataset of over 30 million source-receiver pairs was generated with a Gaussian beam solver across seasonal sound speed profiles and one-third-octave frequency bands spanning 12.5 Hz to 8 kHz. We first assess sparse variational Gaussian processes (SVGP) and then incorporate physics-based mean functions combining spherical spreading with frequency-dependent absorption. To capture nonlinear effects, we examine deep sigma-point processes and stochastic variational deep kernel learning. The final framework integrates four components: (i) a learnable physics-informed mean that represents dominant propagation trends, (ii) a convolutional encoder for bathymetry along the source-receiver track, (iii) a neural encoder for source, receiver, and frequency coordinates, and (iv) a residual SVGP layer that provides calibrated predictive uncertainty. This probabilistic digital twin facilitates the construction of sound-exposure bounds and worst-case scenarios for received levels. We further demonstrate the application of the framework to ship speed optimization, where predicted transmission loss combined with near-field source models provides sound exposure level estimates for minimizing acoustic impacts on marine mammals. The proposed framework advances uncertainty-aware digital twins for ocean acoustics and illustrates how physics-guided machine learning can support sustainable maritime operations.
Problem
Research questions and friction points this paper is trying to address.
Predicting 3D underwater sound transmission loss in realistic ocean environments
Developing probabilistic digital twins for ship noise impact mitigation
Providing uncertainty-aware acoustic exposure estimates for marine life protection
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-guided probabilistic framework predicts underwater transmission loss
Integrates learnable physics-informed mean with deep kernel learning
Combines convolutional encoders with residual SVGP for uncertainty calibration
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2023-12-08arXiv.orgCitations: 0
