Construction of Long-term Seismic Catalog with Deep Learning and
Characterization of Preseismic Fault Behavior in the Ridgecrest-Coso
Region (2008-2019)
Abstract
Long-term seismicity is an effective tool to infer fault properties at
depth, but the catalog construction is challenging because of the large
data volume. We propose a new deep learning-based workflow that follows
a “Train-Detect-Pick” procedure, which solves the generalization
problem in AI pickers. We apply the new workflow on the preseismic phase
(2008-2019) of Ridgecrest-Coso region. Results show that the new
workflow realizes efficient and stable detection, and well substitutes
matched filter. Our new catalog helps characterize the preseismic fault
behavior: (1) the Ridgecrest area has a distributed deformation, and the
2019-ruptured segment has a persistent asperity; (2) the central Garlock
fault is unfavorable for rupture propagation, because of its
discontinuous geometry and low coupling ratio; (3) the Coso geothermal
field generates intense and shallow seismicity, which has a high b-value
that does not correlate with seismicity rate and industrial production,
thus suggest a low stress level.