Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination
arxiv(2024)
Abstract
Subsurface seismic velocity structure is essential for earthquake source
studies, including hypocenter determination. Conventional hypocenter
determination methods ignore the inherent uncertainty in seismic velocity
structure models, and the impact of this oversight has not been thoroughly
investigated. Here, we address this issue by employing a physics-informed deep
learning (PIDL) approach that quantifies uncertainty in seismic velocity
structure modeling and its propagation to hypocenter determination by
introducing neural network ensembles trained on active seismic survey data,
earthquake observation data, and the physical equation of wavefront movement.
An analysis of an earthquake in southwest Japan using our method revealed that
accounting for such uncertainty propagation significantly reduced the bias and
uncertainty underestimation in the hypocenter determination, enabling
quantitative evaluation of the focal depth relative to the plate boundary. Our
results highlight the potential of PIDL for various geophysical inverse
problems, such as investigating earthquake source parameters, which inherently
suffer from uncertainty propagation.
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