Using ancillary information to improve hypocenter estimation: Bayesian single event location (BSEL)

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We have developed and tested an algorithm, Bayesian Single Event Location (BSEL), for estimating the location of a seismic event. The main driver for our research is the inadequate representation of ancillary information in the hypocenter estimation procedure. The added benefit is that we have also addressed instability issues often encountered with historical NLR solvers (e.g., non-convergence or seismically infeasible results). BSEL differs from established nonlinear regression techniques by using a Bayesian prior probability density function (prior PDF) to incorporate ancillary physical basis constraints about event location. P-wave arrival times from seismic events are used in the development. Depth, a ... continued below

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Anderson, Dale N January 1, 2008.

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We have developed and tested an algorithm, Bayesian Single Event Location (BSEL), for estimating the location of a seismic event. The main driver for our research is the inadequate representation of ancillary information in the hypocenter estimation procedure. The added benefit is that we have also addressed instability issues often encountered with historical NLR solvers (e.g., non-convergence or seismically infeasible results). BSEL differs from established nonlinear regression techniques by using a Bayesian prior probability density function (prior PDF) to incorporate ancillary physical basis constraints about event location. P-wave arrival times from seismic events are used in the development. Depth, a focus of this paper, may be modeled with a prior PDF (potentially skewed) that captures physical basis bounds from surface wave observations. This PDF is constructed from a Rayleigh wave depth excitation eigenfunction that is based on the observed minimum period from a spectrogram analysis and estimated near-source elastic parameters. For example, if the surface wave is an Rg phase, it potentially provides a strong constraint for depth, which has important implications for remote monitoring of nuclear explosions. The proposed Bayesian algorithm is illustrated with events that demonstrate its congruity with established hypocenter estimation methods and its application potential. The BSEL method is applied to three events: (1) A shallow Mw 4 earthquake that occurred near Bardwell, KY on June 6, 2003, (2) the Mw 5.6 earthquake of July 26, 2005 that occurred near Dillon, MT, and (3) a deep Mw 5.7 earthquake that occurred off the coast of Japan on April 22, 1980. A strong Rg was observed from the Bardwell, KY earthquake that places very strong constraints on depth and origin time. No Rg was observed for the Dillon, MT earthquake, but we used the minimum observed period of a Rayleigh wave (7 seconds) to reduce the depth and origin time uncertainty. Because the Japan event was deep, there is no observed surface wave energy. We utilize the prior generated from the Dillon, MT event to show that even in the case when a prior is inappropriately applied, high quality data will overcome its influence and result in a reasonable hypocenter estimate.

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  • Journal Name: PAGEOPH Pure and applied geophysics; Journal Volume: 166; Journal Issue: 4

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  • Report No.: LA-UR-08-07992
  • Report No.: LA-UR-08-7992
  • Grant Number: AC52-06NA25396
  • Office of Scientific & Technical Information Report Number: 956661
  • Archival Resource Key: ark:/67531/metadc935530

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  • January 1, 2008

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  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 1:45 p.m.

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Anderson, Dale N. Using ancillary information to improve hypocenter estimation: Bayesian single event location (BSEL), article, January 1, 2008; [New Mexico]. (digital.library.unt.edu/ark:/67531/metadc935530/: accessed October 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.