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

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 M-w 5.4 Chino Hills event. Initial estimates of epicentral location and magnitude are available approximate to 14s after origin time. The estimate can be refined as more data arrive: by approximate to 40s, fault strike and source depth can also be determined with relatively high certainty.