Probabilistic point source inversion of strong-motion data in 3-D media using pattern recognition: A case study for the 2008 M w 5.4 Chino Hills earthquake

Date

2016

Authors

Kaufl, Paul
Valentine, Andrew
Trampert, Jeannot

Journal Title

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Volume Title

Publisher

American Geophysical Union

Abstract

Despite the ever increasing availability of computational power, real-time source inversions based on physical modeling of wave propagation in realistic media remain challenging. We investigate how a nonlinear Bayesian approach based on pattern recognition and synthetic 3-D Green's functions can be used to rapidly invert strong-motion data for point source parameters by means of a case study for a fault system in the Los Angeles Basin. The probabilistic inverse mapping is represented in compact form by a neural network which yields probability distributions over source parameters. It can therefore be evaluated rapidly and with very moderate CPU and memory requirements. We present a simulated real-time inversion of data for the 2008 Mw 5.4 Chino Hills event. Initial estimates of epicentral location and magnitude are available ∼14 s after origin time. The estimate can be refined as more data arrive: by ∼40 s, fault strike and source depth can also be determined with relatively high certainty.

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Citation

Source

Geophysical Research Letters

Type

Journal article

Book Title

Entity type

Access Statement

Open Access

License Rights

DOI

10.1002/2016GL069887

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