Earthquake Declustering Using the Nearest‐Neighbor Approach in Space‐Time‐Magnitude Domain

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 magnitudes m ≥ 4 during 2000-2015; a Southern California catalog with m ≥ 2.5, 3.5 during 1981-2017; an area around the 1992 Landers rupture zone with m ≥ 0.0 during 1981-2015; and the Parkfield segment of San Andreas fault with m ≥ 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.We analyze global seismicity, earthquakes in California, and a synthetic catalog produced by the ETAS model. Table 1 summarizes information about the examined data.

Seasonal Nontectonic Loading Inferred From cGPS as a Potential Trigger for the M6.0 South Napa Earthquake

“…We decluster the seismicity data sets following the algorithm of Zaliapin et al (); Zaliapin and Ben‐Zion (, ), which uses the Baiesi and Paczuski () metric for earthquake correlation in time/space/magnitude to divide the catalog into clustered and declustered populations. We use a version of the published algorithm that ignores the event magnitudes, which has been shown to be more effective for catalogs with large events (Zaliapin & Ben‐Zion, ). The details of this approach and its quality assessment can be found in Zaliapin and Ben‐Zion (, ).…”

Statistical Modeling and Characterization of Induced Seismicity Within the Western Canada Sedimentary Basin

“…As mentioned above, the trimodal appearance of the η distribution (Figure 2b) is unusual; other studies have consistently observed bimodality in earthquake interevent distance distributions across multiple regions and on multiple scales (e.g., Zaliapin & Ben‐Zion, 2020, and references therein). In order to test the veracity of a three‐component mixture, initially chosen from the minimization of the Akaike and Bayesian information criteria, we perform an additional likelihood ratio test (LRT) (Davidson & Mackinnon, 2007; Greene, 2018; McLachlan, 2000).…”

“…Their studies agreed well with the viscoelastic damage model, where a higher degree of interevent triggering and swarm‐like clustering was found within more ductile regions, such as geothermal settings or areas prone to magmatic or dike intrusion (e.g., Farrell et al, 2009; Morita et al, 2006; Sagiya et al, 2002), whereas more brittle rheology tended toward a higher proportion of burst‐like sequences. A more recent study has found that the NND algorithm can be effectively applied as a basis for catalog de‐clustering as well (Zaliapin & Ben‐Zion, 2020).…”

Aftershocks and Background Seismicity in Tangshan and the Rest of North China

“…However, the ETAS model assumes a constant background seismicity rate, which may not hold, as acknowledged by Liu et al. (2020) and suggested by other studies (Zaliapin & Ben‐Zion, 2020).…”