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Physics-informed machine learning for matched field source-range estimation
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PRE
AI
DOI：10.1121/10.0041850.png)
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Abstract
En 中文
A physics-informed machine learning (ML) framework for ocean acoustic source localization using matched field processing (MFP) is presented. A physics-informed neural network (PINN) predicts complex acoustic pressure fields from sparse pressure measurements and a known sound speed profile (SSP). These PINN-predicted replica fields are integrated into the MFP scheme, enabling fine-resolution source-receiver range estimation without requiring detailed geoacoustic bottom parameters. Validation with experimental data from the Shallow Water Evaluation Cell Experiment 1996 (SWellEx-96) demonstrates accurate range estimation, including in the challenging closest point of approach region. The method maintains performance when localizing from array element depths excluded during PINN training and under sparse-array configurations and moderate SSP mismatch. Compared to conventional model-based MFP, the method avoids full environmental characterization and mitigates environmental mismatch effects. Unlike purely data-driven ML methods, it incorporates the governing wave physics, producing physically consistent replicas and improving interpolation/extrapolation to ranges and array element depths that were not used in training. These results highlight the advantages of a physics-informed data-driven approach for ocean acoustic localization in realistic, data-limited environments.
Keywords:
ACOUSTIC SOURCE LOCALIZATION
NEURAL-NETWORK
BAYESIAN OPTIMIZATION
GEOACOUSTIC INVERSION
ARRAY
RECONSTRUCTION
VARIABILITY
FRAMEWORK
COHERENT
MODEL
Journal
J
IF:
2.3
Papers:
585
Citations:
5.1W
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Cited Papers
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