# Assessing the predicting power of GPS data for aftershocks forecasting

arXiv: https://arxiv.org/abs/2305.11183 · HTML: https://mlsanity.com/paper/2305.11183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 47 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 1 of 1)

- [Bayesian Estimation of the ETAS Model for Earthquake Occurrences](https://mlsanity.com/paper/2109.05894/) (2021)

## Abstract (arXiv)

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.