GrowClust: A Hierarchical Clustering Algorithm for Relative Earthquake Relocation, with Application to the Spanish Springs and Sheldon, Nevada, Earthquake Sequences

Abstract

Accurate earthquake locations are essential for providing reliable hazard assessments, understanding the physical mechanisms driving extended earthquake sequences, and interpreting fault structure. Techniques based on waveform cross correlation can significantly improve the precision of the relative locations of event pairs observed at a set of common stations. Here we describe GrowClust, an open‐source, relative relocation algorithm that can provide robust relocation results for earthquake sequences over a wide range of spatial and temporal scales. The method uses input differential travel times, cross‐correlation values, and reference starting locations, and applies a hybrid, hierarchical clustering algorithm to simultaneously group and relocate events within similar event clusters. The method is computationally efficient and numerically stable in its capacity to process large data sets and naturally applies greater weight to more similar event pairs. Additionally, it outputs location error estimates that can be used to help interpret the reliability and resolution of relocation results. As an example, we apply the GrowClust method to the recent Spanish Springs and Sheldon, Nevada, earthquake swarms. These sequences highlight the future potential for applying the GrowClust relocation method on a much larger scale within the region, where existing relocation results are sparse but vital for understanding the seismotectonics and seismic hazard of Nevada and eastern California.