Automatic Earthquake Declustering Using the Nearest‐Neighbor Distance

In the widely adopted description of seismic occurrence, earthquakes are categorized as either background or triggered events. In this work, we present a fully automated, non-parametric algorithm for distinguishing between these two categories, a process known as seismic declustering, based on the widely used nearest-neighbor (NN) metric. We introduce a new measure, the susceptibility index, which identifies an optimal threshold to discriminate between background and triggered events within the NN metric. Through statistical testing on simulated epidemic type aftershock sequence catalogs, we demonstrate that our method yields classification metrics exceeding 90%, outperforming state-of-the art algorithms. Notably, we show that a single threshold is sufficient for reliable discrimination within a given data set. The identification of this threshold requires memory capacity and computational time that scale linearly and quadratically with the data set size, respectively, making the method particurarly suited for large earthquake catalogs. We also apply our method to the relocated Southern California catalog and the GeoNet catalog of New Zealand (NZ). Our method effectively adapts across the different tectonic settings, capturing the variability of background seismicity rates between the shallow crustal events of Southern California and the tectonically diverse seismicity of NZ.