Open AccessMissing Earthquake Data Reconstruction in the Space‐Time‐Magnitude Domain
Author(s) -
Stallone Angela,
Falcone Giuseppe
Publication year - 2021
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2020ea001481
Subject(s) - aftershock , python (programming language) , magnitude (astronomy) , toolbox , seismic hazard , computer science , seismology , earthquake prediction , earthquake rupture , earthquake magnitude , data mining , earthquake simulation , hazard , algorithm , geology , mathematics , fault (geology) , chemistry , physics , geometry , organic chemistry , astronomy , scaling , programming language , operating system
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 M W 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.