Reverse Time Migration (RTM) is a seismic imaging method that reconstructs subsurface reflectivity by back-propagating recorded wavefields through a velocity model using the full two-way wave equation. Unlike conventional migration approaches, RTM can handle all seismic wave types and accurately images complex geological structures with steep dips and strong lateral velocity variations. It is widely used in oil and gas exploration, geophysical research, and subsurface characterization, with active research in velocity model building, imaging conditions, and computational efficiency.

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Event Classification by Physics-Informed Inpainting for Distributed Multichannel Acoustic Sensor with Partially Degraded Channels

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Distributed multichannel acoustic sensing (DMAS) enables large-scale sound event classification (SEC), but performance drops when many channels are degraded and when sensor layouts at test time differ from training layouts. We propose a learning-free, physics-informed inpainting frontend based on reverse time migration (RTM). In this approach, observed multichannel spectrograms are first back-propagated on a 3D grid using an analytic Green’s function to form a scene-consistent image, and then forward-projected to reconstruct inpainted signals before log–mel feature extraction and transformer-based classification. We evaluate the method on ESC-50 with 50 sensors and three layouts (circular, linear, right-angle), where per-channel SNRs are sampled from ?30 to 0 dB. Compared with an AST baseline, scaling-sparsemax channel selection, and channel-swap augmentation, the proposed RTM frontend achieves the best or competitive accuracy across all layouts, improving accuracy by 13.1 points on the right-angle layout (from 9.7% to 22.8%). Correlation analyses show that spatial weights align more strongly with SNR than with channel–source distance, and that higher SNR–weight correlation corresponds to higher SEC accuracy. These results demonstrate that a reconstruct-then-project, physics-based preprocessing effectively complements learning-only methods for DMAS under layout-open configurations and severe channel degradation.

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