Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability
In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package. We here extend this previous work in two key ways, focusing on data from Japan as a large dataset is necessary in order to allow for the training of a deep learning (autoencoder) model. First, we move from whole-region prediction (considering, for each candidate event, the likelihood of an event M $\geq$ 5.0 anywhere in the region in the next 15 days) to localised predictions in which both the reg...
| Authors | Wei Quan, Denise Gorse |
|---|---|
| Primary category | cs.LG |
| Published | 8 June 2026 |
| Last updated at source | 9 September 2026 |
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