A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- ENSCO, Inc., Falls Church, VA (United States)
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.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1236239
- Report Number(s):
- SAND--2015-2715J; 582023
- Journal Information:
- Bulletin of the Seismological Society of America, Journal Name: Bulletin of the Seismological Society of America Journal Issue: 5 Vol. 105; ISSN 0037-1106
- Publisher:
- Seismological Society of AmericaCopyright Statement
- Country of Publication:
- United States
- Language:
- English
PhaseLink: A Deep Learning Approach to Seismic Phase Association
|
journal | January 2019 |
| PhaseLink: A Deep Learning Approach to Seismic Phase Association | text | January 2018 |
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