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NESTOREv1.0: A MATLAB Package for Strong Forthcoming Earthquake Forecasting

Authors :
Stefania Gentili
Piero Brondi
Rita Di Giovambattista
Source :
Seismological Research Letters.
Publication Year :
2023
Publisher :
Seismological Society of America (SSA), 2023.

Abstract

This article presents the first publicly available version of the NExt STrOng Related Earthquake (NESTORE) software (NESTOREv1.0) designed for the statistical analysis of earthquake clusters. NESTOREv1.0 is a MATLAB (www.mathworks.com/products/matlab, last accessed August 2022) package capable of forecasting strong aftershocks starting from the first hours after the mainshocks. It is based on the NESTORE algorithm, which has already been successfully applied retrospectively to Italian and California seismicity. The code evaluates a set of features and uses a supervised machine learning approach to provide probability estimates for a subsequent large earthquake during a seismic sequence. By analyzing an earthquake catalog, the software identifies clusters and trains the algorithm on them. It then uses the training results to obtain forecasting for a test set of independent data to estimate training performance. After appropriate testing, the software can be used as an Operational Earthquake Forecasting (OEF) method for the next stronger earthquake. For ongoing clusters, it provides near-real-time forecasting of a strong aftershock through a traffic light classification aimed at assessing the level of concern. This article provides information about the NESTOREv1.0 algorithm and a guide to the software, detailing its structure and main functions and showing the application to recent seismic sequences in California. By making the NESTOREv1.0 software available, we hope to extend the impact of the NESTORE algorithm and further advance research on forecasting the strongest earthquakes during seismicity clusters.

Subjects

Subjects :
Geophysics

Details

ISSN :
19382057 and 08950695
Database :
OpenAIRE
Journal :
Seismological Research Letters
Accession number :
edsair.doi...........408a8cf82a9d06fc23a3be5d816c22ca