Learning earthquake sources using symmetric autoencoders

Abstract

This study examines almost thirty deep-focus earthquakes, magnitudes starting from Mw 6.0 and higher, with the aim of accurately determining the source-time function (STF) of P arrival and its azimuthal dependence. We use the variational symmetric autoencoder (SymVAE), a neural network architecture designed to automatically isolate earthquake information from far-field seismic waves. Our findings demonstrate that the STFs produced by the network uncover weak secondary episodes in numerous earthquakes, providing evidence that the majority deep-focus earthquakes release bursts of seismic moment. This groundbreaking study is the first to generate high resolution STFs without requiring traditional path-effect deconvolution, a process that usually introduces substantial uncertainties and hinders achieving high temporal resolution. Our unsupervised learning method for obtaining STFs does not require labeled seismograms and is based on the principle of scale separation, which allows the accumulation of earthquake information from nearby receivers. This principle states that the variations in far-field band-limited seismic measurements resulting from finite faulting occur across two spatial scales: a slower scale associated with the source processes and a faster scale corresponding to path effects. This research compares the STFs obtained from SymVAE with those gathered by stacking envelopes and traditional deconvolution. We evaluated the quality of SymVAE output and performed a synthetic experiment to recover the source in the presence of path scattering.