Poster Presentation

Quantifying the degree of source overlap between potential repeating earthquake pairs has been identified as important test for the identification of the repeating earthquakes. Assessment is commonly based on their separation, reported uncertainty, and a fixed stress drop - often 3 MPa - to represent the extent of the ruptures. However, neither of these approaches provides a probabilistic understanding of the overlap between potential repeating events. To quantify the probability of overlap, uncertainties in both the locations and source dimensions need to be taken into account. In this work, we present a probabilistic approach for assessing earthquake overlap, wherein centroid locations for event pairs are perturbed based on their joint location uncertainties and covariances. To understand the importance of the covariance in those location perturbations, we conduct synthetic experiments based on covariance matrices derived from a synthetic joint location problem and single-layer implementations of hypoDD. Our results suggest that properly accounting for location covariance and source dimension uncertainties leads to substantial differences in the identification of repeating earthquakes