DOI: 10.1002/2016GL069887
论文题名: 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
作者: Käufl P. ; Valentine A.P. ; Trampert J.
刊名: Geophysical Research Letters
ISSN: 0094-8766
EISSN: 1944-8497
出版年: 2016
卷: 43, 期: 16 起始页码: 8492
结束页码: 8498
语种: 英语
英文关键词: earthquake early warning
; neural networks
; probabilistic inversion
Scopus关键词: Bayesian networks
; Earthquakes
; Faulting
; Geophysics
; Neural networks
; Pattern recognition
; Pattern recognition systems
; Wave propagation
; Bayesian approaches
; Computational power
; Earthquake early warning
; Los Angeles Basin
; Memory requirements
; Probabilistic inversion
; Source parameters
; Strong motion datum
; Probability distributions
英文摘要: 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. ©2016. The Authors. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983627452&doi=10.1002%2f2016GL069887&partnerID=40&md5=bd751452644c8cf461f325924e44f0a5
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/9729
Appears in Collections: 科学计划与规划 气候变化与战略
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作者单位: Department of Earth Sciences, Utrecht University, Utrecht, Netherlands
Recommended Citation:
Käufl P.,Valentine A.P.,Trampert J.. 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[J]. Geophysical Research Letters,2016-01-01,43(16).