Greg Beroza, Mostafa Mousavi, and Weiqiang Zhu.

Deep Learning of Earthquake Signals

Gregory C. Beroza, S. Mostafa Mousavi, and Weiqiang Zhu

Diverse algorithms have been developed for efficient earthquake signal detection and processing. These algorithms are becoming increasingly important as seismologists strive to extract as much insight as possible from exponentially increasing volumes of continuous seismic data. Waveform similarity search, based on the premise that adjacent earthquakes generate similar waveforms, is now widely and effectively used to detect earthquakes too small to appear routinely in earthquake catalogs. Machine learning has the potential to generalize this similarity search from strict waveform similarity to waveforms that have similar characteristics. Convolutional and recurrent networks have each been shown to be promising tools for earthquake signal detection, and we have developed a deep convolutional-recurrent network to combine the advantages of each. This architecture is well-suited to learn both the spectral and temporal characteristics of earthquake signals. We have applied it to different, but inter-related tasks in earthquake analysis, including: earthquake detection, classification of continuous seismic data into P-waves, S-waves, and noise, and the problem of de-noising of earthquake signals. In our presentation we demonstrate the performance of deep learning applied to seismic signals for each of these tasks.