A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location

Draelos, Timothy J.; Ballard, Sanford; Young, Christopher J.; Brogan, Ronald

doi:10.1785/0120150099

Title: A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location

Abstract

Given a set of observations within a specified time window, a fitness value is calculated at each grid node by summing station-specific conditional fitness values. Assuming each observation was generated by a refracted P wave, these values are proportional to the conditional probabilities that each observation was generated by a seismic event at the grid node. The node with highest fitness value is accepted as a hypothetical event location, subject to some minimal fitness value, and all arrivals within a longer time window consistent with that event are associated with it. During the association step, a variety of different phases are considered. In addition, once associated with an event, an arrival is removed from further consideration. While unassociated arrivals remain, the search for other events is repeated until none are identified.

Authors:

Draelos, Timothy J. ^[1]; Ballard, Sanford ^[1]; Young, Christopher J. ^[1]; Brogan, Ronald ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
ENSCO, Inc., Falls Church, VA (United States)

Publication Date:: Thu Oct 01 00:00:00 EDT 2015

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1236239

Report Number(s):: SAND-2015-2715J
Journal ID: ISSN 0037-1106; 582023

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Accepted Manuscript

Journal Name:: Bulletin of the Seismological Society of America

Additional Journal Information:: Journal Volume: 105; Journal Issue: 5; Journal ID: ISSN 0037-1106

Publisher:: Seismological Society of America

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES

Citation Formats


                    Draelos, Timothy J., Ballard, Sanford, Young, Christopher J., and Brogan, Ronald. A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location.  United States: N. p., 2015. 
Web.  doi:10.1785/0120150099.

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                    Draelos, Timothy J., Ballard, Sanford, Young, Christopher J., & Brogan, Ronald. A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location.  United States.  https://doi.org/10.1785/0120150099

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                    Draelos, Timothy J., Ballard, Sanford, Young, Christopher J., and Brogan, Ronald. Thu .  
"A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location".  United States.  https://doi.org/10.1785/0120150099.  https://www.osti.gov/servlets/purl/1236239.

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@article{osti_1236239,

  title        = {A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location},

  author       = {Draelos, Timothy J. and Ballard, Sanford and Young, Christopher J. and Brogan, Ronald},

  abstractNote = {Given a set of observations within a specified time window, a fitness value is calculated at each grid node by summing station-specific conditional fitness values. Assuming each observation was generated by a refracted P wave, these values are proportional to the conditional probabilities that each observation was generated by a seismic event at the grid node. The node with highest fitness value is accepted as a hypothetical event location, subject to some minimal fitness value, and all arrivals within a longer time window consistent with that event are associated with it. During the association step, a variety of different phases are considered. In addition, once associated with an event, an arrival is removed from further consideration. While unassociated arrivals remain, the search for other events is repeated until none are identified.},

  doi          = {10.1785/0120150099},

  journal      = {Bulletin of the Seismological Society of America},

  number       = 5,

  volume       = 105,

  place        = {United States},

  year         = {Thu Oct 01 00:00:00 EDT 2015},

  month        = {Thu Oct 01 00:00:00 EDT 2015}

}

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Works referencing / citing this record:

PhaseLink: A Deep Learning Approach to Seismic Phase Association
text, January 2018

Ross, Zachary E.; Yue, Yisong; Meier, Men-Andrin
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DOI: 10.48550/arxiv.1809.02880

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