Assessing the predicting power of GPS data for aftershocks forecasting
This work addresses earthquake forecasting for seismologists and disaster management, but it is incremental as it applies an existing CNN method to a new data type with limitations in coverage.
The authors tackled aftershock forecasting for Japanese earthquakes from 2015 to 2019 by using GPS data as input to a Convolutional Neural Network, achieving promising performance that depends on GPS station density, with predictive power lost for offshore events.
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.