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Abstract 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 magnitudesm≥ 4 during 2000–2015; a Southern California catalog withm≥ 2.5, 3.5 during 1981–2017; an area around the 1992 Landers rupture zone withm≥ 0.0 during 1981–2015; and the Parkfield segment of San Andreas fault withm≥ 1.0 during 1984–2014. The null hypotheses of stationarity and space‐time independence are not rejected by several tests applied to the estimated background events of the global and Southern California catalogs with magnitude ranges Δm< 4. However, both hypotheses are rejected for catalogs with larger range of magnitudes Δm> 4. The deviations from the nulls are mainly due to local temporal fluctuations of seismicity and activity switching among subregions; they can be traced back to the original catalogs and represent genuine features of background seismicity.
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Perspectives on Clustering and Declustering of Earthquakes
Abstract Clustering is a fundamental feature of earthquakes that impacts basic and applied analyses of seismicity. Events included in the existing short-duration instrumental catalogs are concentrated strongly within a very small fraction of the space–time volume, which is highly amplified by activity associated with the largest recorded events. The earthquakes that are included in instrumental catalogs are unlikely to be fully representative of the long-term behavior of regional seismicity. We illustrate this and other aspects of space–time earthquake clustering, and propose a quantitative clustering measure based on the receiver operating characteristic diagram. The proposed approach allows eliminating effects of marginal space and time inhomogeneities related to the geometry of the fault network and regionwide changes in earthquake rates, and quantifying coupled space–time variations that include aftershocks, swarms, and other forms of clusters. The proposed measure is used to quantify and compare earthquake clustering in southern California, western United States, central and eastern United States, Alaska, Japan, and epidemic-type aftershock sequence model results. All examined cases show a high degree of coupled space–time clustering, with the marginal space clustering dominating the marginal time clustering. Declustering earthquake catalogs can help clarify long-term aspects of regional seismicity and increase the signal-to-noise ratio of effects that are subtler than the strong clustering signatures. We illustrate how the high coupled space–time clustering can be decreased or removed using a data-adaptive parsimonious nearest-neighbor declustering approach, and emphasize basic unresolved issues on the proper outcome and quality metrics of declustering. At present, declustering remains an exploratory tool, rather than a rigorous optimization problem, and selecting an appropriate declustering method should depend on the data and problem at hand.
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- 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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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1785/0220210127
