arXiv Machine Learning By Wei Quan, Denise Gorse

Spatiotemporal Seismic Hazard Assessment Using VQ-VAE and Seismic Statistical Features

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arXiv:2606. 10069v1 Announce Type: new 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.

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arXiv Machine Learning
Aug 4

Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability

arXiv:2606. 10069v3 Announce Type: replace 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.

By Wei Quan, Denise Gorse
arXiv Machine Learning
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GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series

arXiv:2606. 07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle.

By Nick Teutschmann (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Laura Crocetti (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Fanny Lehmann (ETH AI Center, Switzerland), Leonardo Trentini (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Benedikt Soja (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland)
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DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings

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