Full-waveform LiDARに対する深層学習手法を応用した空間的な整合性を考慮した地震波検測

抄録

This paper presents a deep learning approach for P- and S-wave picking from seismic waveforms by applying spatial feature extraction inspired by full-waveform LiDAR data, where observation points and recorded waveforms resemble point clouds and LiDAR signals. Using FWNet++, a model for full-waveform LiDAR data, we extract spatial features from seismic waveforms at each observation point for wave picking, supported by a discriminator to verify spatial consistency against ground truth. The method incorporates a refinement strategy for rule-based results and shows lower residual errors than existing libraries. However, high residuals persist when P- and S-waves are visually indistinct, suggesting a need for seismological rules, such as epicenter distance, to enhance performance. Future research should integrate seismological insights with AI to develop more effective automated picking methods.