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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Detecting and Locating Seismic Events Without Phase Picks or Velocity Models.
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Detecting and Locating Seismic Events Without Phase Picks or Velocity Models.
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A top-down hierarchical approach to the display and analysis of seismic data
Seismic monitoring of a CTBT (Comprehensive Test Ban Treaty) will require analysts to review tens of events per hour recorded by networks of more than a hundred stations. Use of the traditional waveform display as the primary data display tool is incompatible with this requirement; traditional waveform displays are inefficient in their presentation of relevant data and place high demands on computer resources. Drawing on resident data visualization expertise and on our hands-on experience with the design and implementation of the ADSN (AFRAC Distributed Subsurface Network) system at AFTAC, we have designed a new system consisting of a hierarchical seriesmore »