Septor: Seismic Depth Estimation Using Hierarchical Neural Networks

Abstract

The depth of a seismic event is an essential feature to discriminate natural earthquakes from events induced or created by humans. However, estimating the depth of a seismic event with a sparse set of seismic stations is a daunting task, and there is no globally usable method. This paper focuses on developing a machine learning model to accurately estimate the depth of arbitrary seismic events directly from seismograms. Our proposed deep learning architecture is not-so-deep compared to commonly found models in the literature for related tasks, consisting of two loosely connected levels of neural networks, associated with the seismic stations at the higher level and the individual channels of a station at the lower level. Thus, the model has significant advantages, including a reduced number of parameters for tuning and better interpretability to geophysicists. We evaluate our solution on seismic data collected from the SCEDC (Southern California Earthquake Data Center) catalog for regional events in California. The model can learn waveform features specific to a set of stations, while it struggles to generalize to completely novel sets of event sources and stations. In a simplified setting of separating shallow events from deep ones, the model achieved an 86.5% F1-score using the Southern California stations.