Earth and Space Science (Aug 2021)
Missing Earthquake Data Reconstruction in the Space‐Time‐Magnitude Domain
Abstract
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 MW 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.
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