Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability
Updated 7 h ago · first seen 11 Sept 2026
paper_01M294FS8V9FAHXW9Y4RFNCBYS
- Published
- 11 Sept 2026
- T1 · 7 h ago
- arXiv
- 2606.10069
- T1 · 7 h ago
- Category
- cs.LG
- T1 · 7 h ago
Abstract
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 region of feature computation and the region of prediction are restricted to a circle of radius 24 km around the candidate event, and we show that performance remains excellent, similar to our previous whole-region study for the same area. Second, we here couple this proven set of SSFs, based on one-dimensional (catalogue) data, with a novel feature based on magnitude-stratified two-dimensional seismic maps, obtained by training a VQ-VAE model to reproduce such maps as output and identifying a measure of its error in doing so with anomalies in magnitude distribution reflecting a localised build-up of crustal stress. We show that while localised prediction based only on a set of SSFs can be effective, with test AUC values as high as those obtained in the case of Japan in our previous whole-region study, the inclusion of the new natively-spatial VQ-VAE-derived feature, top-ranked by SHAP analysis, both improves prediction and points toward a new approach that might allow computationally time-demanding traditional seismic features to be entirely set aside.
Authors 2
Wei Quan, Denise Gorse
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Arxiv announce type
- replace
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- arXiv id
- 2606.10069
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Categories
- cs.LG, physics.geo-ph
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
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Provenance
Attributed facts
9
Source tiers
T19
Freshest observation
7 h ago
Conflicts
None
No models linked to this paper yet.
- Authors
- Wei Quan, Denise Gorse
As of
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Claim history · Official page
Official pageofficial_url1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| https://arxiv.org/abs/2606.10069 | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
- New paperPaperUsing Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability
New paper: Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 5 h ago | 1 |
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