Assessing the predicting power of GPS data for aftershocks forecasting
Geophysics
2023-05-22 v1 Machine Learning
Data Analysis, Statistics and Probability
Abstract
We present a machine learning approach for the aftershock forecasting of Japanese earthquake catalogue from 2015 to 2019. Our method takes as sole input the ground surface deformation as measured by Global Positioning System (GPS) stations at the day of the mainshock, and processes it with a Convolutional Neural Network (CNN), thus capturing the input's spatial correlations. Despite the moderate amount of data the performance of this new approach is very promising. The accuracy of the prediction heavily relies on the density of GPS stations: the predictive power is lost when the mainshocks occur far from measurement stations, as in offshore regions.
Keywords
Cite
@article{arxiv.2305.11183,
title = {Assessing the predicting power of GPS data for aftershocks forecasting},
author = {Vincenzo Maria Schimmenti and Giuseppe Petrillo and Alberto Rosso and Francois P. Landes},
journal= {arXiv preprint arXiv:2305.11183},
year = {2023}
}
Comments
15 pages main + appendix. 3 figures main, 2 appendix