NESTORE

NESTORE is a MATLAB package capable to estimate, during ongoing of an aftershock sequence following a damaging earthquake, the likelihood of

NESTORE PACKAGE README

MATLAB package able to forecast strong aftershocks starting from the first hours after the mainshocks

Software Description

NESTORE is a MATLAB package capable to estimate, during ongoing of an aftershock sequence following a damaging earthquake, the likelihood of the occurrence of another strong earthquake.

The code is based on the seismicity characteristics and uses a machine learning approach to provide forecasting for the ongoing seismic sequence.

Starting from an input catalogue, the package

1. Provides an identification of clusters by a window based method (Cluster Identification Module)
2. Trains the algorithm by machine learning on cluster's features (Training Module)
3. Tests the algorithm performances (Testing Module)
4. Classifies in near-real-time new clusters (Near-Real-Time Classification Module)

For further details see the paper: NESTOREv1.0: A Matlab package to identify patterns for strong following earthquake forecasting S. Gentili, P. Brondi, R. Di Giovambattista

Installation

Download NESTOREv1.0.zip and extract in a folder you prefer (e.g. NESTORE_FOLDER)or clone NESTORE repository on your computer; no other action is required. NESTORE has been tested on Matlab R2018a and later versions.

Usage

To run NESTORE code start MATLAB, move in your sub-directory NESTOREv1.0/user(e.g. NESTORE_FOLDER/NESTOREv1.0/user); in the MATLAB command line, type the corresponding run you need (e.g. run_training); examples are provided. See Folder_struc.txt and Fileinput_format.txt for further details.

Software Support

This package is the first online version of NESTORE, so any suggestions or bug reporting are welcome. Please contact [e-mail]

Credits

Please use the following citation for any use of this software: Stefania Gentili, Piero Brondi, Rita Di Giovambattista; NESTOREv1.0: A MATLAB Package for Strong Forthcoming Earthquake Forecasting. Seismological Research Letters 2023; doi: https://doi.org/10.1785/0220220327

References

• Gentili, S., and R. Di Giovambattista (2017). Pattern recognition approach to the subsequent event of damaging earthquakes in Italy, Phys. Earth Planet. In. 266, 1–17.

• Gentili, S., and R. Di Giovambattista (2020). Forecasting strong aftershocks in earthquake clusters from northeastern Italy and western Slovenia, Phys. Earth Planet. In. 303, doi: 10.1016/j.pepi.2020.106483.

• Gentili, S., and R. Di Giovambattista (2022). Forecasting strong subsequent earthquakes in California clusters by machine learning, Phys. Earth Planet. In. 327, doi: 10.1016/j.pepi.2022.106879.

• Gentili, S., E. Anyfadi, P. Brondi, and F. Vallianatos (2023). Forecasting strong subsequent earthquakes in Greece using NESTORE machine learning algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 April 2023.

Acknowledgments

The NESTORE software improvement for making it more robust and for distributing to the scientific community has been funded by a grant from the Italian Ministry of Foreign Affairs and International Cooperation.

License

GNU General Public License as published by the Free Software Foundation; version 3 of the License or any later version.