Earthquake catalog declustering is the procedure of separating event clusters from background seismicity, which is an important task in statistical seismology, earthquake forecasting, and probabilistic seismic hazard analysis. Several declustering methods have been introduced in the literature and operate under the supposition that background events occur independently whereas clusters are triggered by prior events. Here, we test the ability of supervised machine learning (SML) on the declustering problem by leveraging two popular statistical methods. First, the epidemic‐type aftershock sequence model is fit to a target catalog and the parameters are used to generate synthetic earthquake data, which replicate the magnitude–space–time seismicity of the target catalog. Next, the nearest‐neighbor distance (NND) metrics are computed between each simulated event and used as features to train the SML algorithm. Finally, the trained algorithm is applied to decluster synthetic testing data and then the original target catalog. Our results indicate that the SML method performs better than the NND‐based and stochastic declustering methods on the test data and makes more nuanced selections of background and clustered events when applied to real seismicity. Although the vast majority of the SML technique’s predictive power appears to lie within the NND values of the “first” nearest neighbors, a machine learning analysis reveals that predictive accuracy can be improved by additional “next” nearest neighbors and differential magnitude features. The developed approach is applied to seismic catalogs in southern California and Italy to decluster them.

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