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Title: Summer Intern Report: Multiple-Event Seismic Location Using Bayesian Inversion: Case Study Using Newberry Enhanced Geothermal System Data

Abstract

The central idea of the method used in the work is representing all the information we have using probability densities and then using inversion method to infer the A posteriori probability density of the model parameters we would like to know from all these information we have. This information includes forward model (the assumption we made on the problem and the theory we use for arrival time calculation), observations (arrival time), and A prior information on the model parameters (all other information we have on model parameters before the inversion).

Authors:
 [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1438634
Report Number(s):
LLNL-TR-667352
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Zhang, Zhishuai. Summer Intern Report: Multiple-Event Seismic Location Using Bayesian Inversion: Case Study Using Newberry Enhanced Geothermal System Data. United States: N. p., 2015. Web. doi:10.2172/1438634.
Zhang, Zhishuai. Summer Intern Report: Multiple-Event Seismic Location Using Bayesian Inversion: Case Study Using Newberry Enhanced Geothermal System Data. United States. doi:10.2172/1438634.
Zhang, Zhishuai. Tue . "Summer Intern Report: Multiple-Event Seismic Location Using Bayesian Inversion: Case Study Using Newberry Enhanced Geothermal System Data". United States. doi:10.2172/1438634. https://www.osti.gov/servlets/purl/1438634.
@article{osti_1438634,
title = {Summer Intern Report: Multiple-Event Seismic Location Using Bayesian Inversion: Case Study Using Newberry Enhanced Geothermal System Data},
author = {Zhang, Zhishuai},
abstractNote = {The central idea of the method used in the work is representing all the information we have using probability densities and then using inversion method to infer the A posteriori probability density of the model parameters we would like to know from all these information we have. This information includes forward model (the assumption we made on the problem and the theory we use for arrival time calculation), observations (arrival time), and A prior information on the model parameters (all other information we have on model parameters before the inversion).},
doi = {10.2172/1438634},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Feb 17 00:00:00 EST 2015},
month = {Tue Feb 17 00:00:00 EST 2015}
}

Technical Report:

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