A novel deep-learning image condition for locating earthquake

Abstract

SUMMARY Migration-based earthquake location methods may encounter the polarity reversal issue due to the non-explosive components of seismic sources, leading to an unfocused migration image. Such a problem usually makes it difficult to accurately retrieve the optimal location from the migrated source image. In this study, by taking advantage of the general pattern recognition ability of the convolutional neural network, we propose a novel deep-learning image condition (DLIC) to address this issue. The proposed DLIC measures the goodness of waveform alignments for both P and S waves, and it follows the geophysical principle of seismic imaging that the best-aligned waveforms represent fully a best-imaged source location. A synthetic test shows that the DLIC can effectively overcome the polarity reversal issues. Real data applications to southern California show that the DLIC can enhance the focusing of the migrated source image over the classic source scanning algorithm. Further tests show that the DLIC applies to continuous seismic data, to regions with few previously recorded earthquakes, and has the potential to locate small earthquakes. The proposed DLIC shall benefit the migration-based source location methods.