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Forecasting Strong Subsequent Earthquakes in Greece with the Machine Learning Algorithm NESTORE.

Eleni-Apostolia Anyfadi^1,2, Stefania Gentili³, Piero Brondi³

¹Section of Geophysics-Geothermics, Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, 15784 Athens, Greece.

Entropy (Basel, Switzerland)

|May 27, 2023

Summary

This study forecasts strong aftershock probability using the NESTORE machine learning approach for Greek seismicity. The method accurately identifies dangerous aftershock clusters, crucial for seismic risk mitigation.

Keywords:

Greek seismicity NESTORE aftershocks clusters features forecasting machine learning algorithm training procedure

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Area of Science:

Geophysics
Seismology
Machine Learning

Background:

Earthquake aftershocks pose significant risks to urban infrastructure and can worsen damage to already weakened structures.
Effective seismic risk mitigation requires methods to forecast the probability of stronger aftershock events.

Purpose of the Study:

To apply the NESTORE machine learning approach to Greek seismicity data (1995-2022) for forecasting strong aftershock probabilities.
To evaluate the NESTORE algorithm's performance in classifying aftershock clusters and its utility for seismic risk mitigation.

Main Methods:

Utilized the NESTORE machine learning algorithm on Greek seismicity data from 1995 to 2022.
Classified aftershock clusters into Type A (more dangerous, smaller magnitude difference) and Type B based on mainshock-aftershock magnitude differences.
Employed region-dependent training and evaluated performance on an independent test set.

Main Results:

Achieved optimal forecasting results 6 hours after the mainshock.
Correctly forecasted 92% of all clusters, including 100% of Type A and over 90% of Type B clusters.
Demonstrated accurate cluster detection across a large area of Greece.

Conclusions:

The NESTORE approach is effective for forecasting strong aftershocks in Greece.
The algorithm's high accuracy and rapid forecasting time make it attractive for seismic risk mitigation strategies.
Successful application in Greece indicates potential for broader use in similar seismically active regions.