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PhysLoc‐DeepONet: Physics‐Constrained DeepONet for Passive Seismic Source Localization With Limited Differential Travel‐Time Data

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PhysLoc-DeepONet achieves high-precision source localization, even with limited seismic data under resource constraints.
Innovations in training data construction and loss function optimization help maintain localization accuracy despite data sparsity.
The model integrates physical constraints with deep learning techniques, addressing challenges faced by existing localization methods.
Multiple numerical simulations and field tests demonstrate the model's adaptability to various monitoring conditions and data quality.

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Proposing PhysLoc-DeepONet improves localization accuracy in passive sources with limited seismic data, suggesting enhanced adaptability.

Zeng et al. (2025) studied this question.

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