1 research outputs found
Learning earthquake sources using symmetric autoencoders
We introduce Symmetric Autoencoder (SymAE), a neural-network architecture
designed to automatically extract earthquake information from far-field seismic
waves. SymAE represents the measured displacement field using a code that is
partitioned into two interpretable components: source and path-scattering
information. We achieve this source-path representation using the scale
separation principle and stochastic regularization, which traditional
autoencoding methods lack. According to the scale separation principle, 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. Once
trained, SymAE facilitates the generation of virtual seismograms, engineered to
not contain subsurface scattering effects. We present time-reversal imaging of
virtual seismograms to accurately infer the kinematic rupture parameters
without knowledge of empirical Green's function. SymAE is an unsupervised
learning method that can efficiently scale with large amounts of seismic data
and does not require labeled seismograms, making it the first framework that
can learn from all available previous earthquakes to accurately characterize a
given earthquake. The paper presents the results of an analysis of nearly
thirty complex earthquake events, revealing differences between earthquakes in
energy rise times, stopping phases, and providing insights into their rupture
complexity