Assessing the predicting power of GPS data for aftershocks forecasting
Vincenzo Maria Schimmenti Giuseppe Petrillo Alberto Rosso Francois P. Landes
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.