Seismic wave detection from seismic data

Abstract

We developed a fine-tuned deep learning model using SegPhase to pick seismic wave travel times from data observed by MeSO-net. The SegPhase model was fine-tuned using MeSO-net data to adapt its pre-trained capabilities to the limited dataset of 522 events and 13 observation points. Evaluation showed that fine-tuning significantly reduced evaluation function value compared to the pre-trained model. All P-waves were detected at a threshold of 0.1, with similarly high performance for S-waves.