We introduce an algorithm for declustering earthquake catalogs based on the nearest‐neighbor analysis of seismicity. The algorithm discriminates between background and clustered events by random thinning that removes events according to a space‐varying threshold. The threshold is estimated using randomized‐reshuffled catalogs that are stationary, have independent space and time components, and preserve the space distribution of the original catalog. Analysis of catalog produced by the Epidemic Type Aftershock Sequence model demonstrates that the algorithm correctly classifies over 80% of background and clustered events, correctly reconstructs the stationary and space‐dependent background intensity, and shows high stability with respect to random realizations (over 75% of events have the same estimated type in over 90% of random realizations). The declustering algorithm is applied to the global Northern California Earthquake Data Center catalog with magnitudes
- NSF-PAR ID:
- 10338873
- Date Published:
- Journal Name:
- Seismological Research Letters
- Volume:
- 93
- Issue:
- 1
- ISSN:
- 0895-0695
- Page Range / eLocation ID:
- 386 to 401
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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