Hierarchical Bayesian Approach to Locating Seismic Events
Abstract
We propose a hierarchical Bayesian model for conducting inference on the location of multiple seismic events (earthquakes) given data on the arrival of various seismic phases to sensor locations. The model explicitly accounts for the uncertainty associated with a theoretical seismic-wave travel-time model used along with the uncertainty of the arrival data. Posterior inferences is carried out using Markov chain Monte Carlo (MCMC).
- Authors:
- Publication Date:
- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 888581
- Report Number(s):
- UCRL-PROC-217057
TRN: US200618%%323
- DOE Contract Number:
- W-7405-ENG-48
- Resource Type:
- Conference
- Resource Relation:
- Conference: Presented at: 2005 Joint Statistical Meetings, Minneapolis, MN, United States, Aug 07 - Aug 11, 2005
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; EARTHQUAKES; EPICENTERS; DATA PROCESSING; MATHEMATICAL MODELS; MONTE CARLO METHOD
Citation Formats
Johannesson, G, Myers, S C, and Hanley, W G. Hierarchical Bayesian Approach to Locating Seismic Events. United States: N. p., 2005.
Web.
Johannesson, G, Myers, S C, & Hanley, W G. Hierarchical Bayesian Approach to Locating Seismic Events. United States.
Johannesson, G, Myers, S C, and Hanley, W G. Wed .
"Hierarchical Bayesian Approach to Locating Seismic Events". United States.
doi:. https://www.osti.gov/servlets/purl/888581.
@article{osti_888581,
title = {Hierarchical Bayesian Approach to Locating Seismic Events},
author = {Johannesson, G and Myers, S C and Hanley, W G},
abstractNote = {We propose a hierarchical Bayesian model for conducting inference on the location of multiple seismic events (earthquakes) given data on the arrival of various seismic phases to sensor locations. The model explicitly accounts for the uncertainty associated with a theoretical seismic-wave travel-time model used along with the uncertainty of the arrival data. Posterior inferences is carried out using Markov chain Monte Carlo (MCMC).},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Nov 09 00:00:00 EST 2005},
month = {Wed Nov 09 00:00:00 EST 2005}
}
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