Missing Earthquake Data Reconstruction in the Space‐Time‐Magnitude Domain

Abstract

Short term aftershock incompleteness (STAI) can strongly bias any analysis built on the assumption that seismic catalogs have a complete record of events. Despite several attempts to tackle this issue, we are far from trusting any data set in the immediate future of a large shock occurrence. Here, we introduce RESTORE (REal catalogs STOchastic REplenishment), a Python toolbox implementing a stochastic gap-filling method, which automatically detects the STAI gaps and reconstructs the missing events in the space-time-magnitude domain. The algorithm is based on empirical earthquake properties and relies on a minimal number of assumptions about the data. Through a numerical test, we show that RESTORE returns an accurate estimation of the number of missed events and correctly reconstructs their magnitude, location, and occurrence time. We also conduct a real-case test, by applying the algorithm to the urn:x-wiley:23335084:media:ess2915:ess2915-math-0001 6.2 Amatrice aftershocks sequence. The STAI-induced gaps are filled and missed earthquakes are restored in a way which is consistent with data. RESTORE, which is made freely available, is a powerful tool to tackle the STAI issue, and will hopefully help to implement more robust analyses for advancing operational earthquake forecasting and seismic hazard assessment.