arXiv AI
Event-Centric Earthquake Monitoring with a Multi-Station Transformer
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arXiv Machine Learning
Jun 10
Spatiotemporal Seismic Hazard Assessment Using VQ-VAE and Seismic Statistical Features
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.
arXiv Machine Learning
Aug 26
SeisMamba: Low-Latency Single-Station Seismic Magnitude Estimation for Spatially Distributed Earthquake Early Warning
arXiv:2608.24561v1 Announce Type: new Abstract: Rapid earthquake magnitude estimation is central to earthquake early warning, yet many operational systems depend on dense regional seismic networks an...
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.
arXiv Machine Learning
Jun 9
Observation-driven correction of numerical weather prediction for marine winds
arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.
arXiv AI
Sep 28
AUWave: A Data-Driven Model for Reconstructing Significant Wave Heights Using Sparse Observations
AUWave is a hybrid deep‑learning framework that reconstructs high‑resolution regional significant wave height (SWH) fields from sparse buoy observations. By combining a station‑wise encoder with a multi‑scale U‑Net enhanced by self‑attention, it outperforms a baseline model, especially when more than one buoy is available, and identifies critical anchor stations through buoy ablation studies. Cross‑basin tests in the Atlantic and Pacific demonstrate the model’s robustness and portability, suggesting its applicability for operational ocean monitoring and data assimilation.
arXiv AI
Jun 16
Learning Earthquake Wave Arrival Time Picking from Labels with Inaccuracies
arXiv:2606. 15377v1 Announce Type: cross Abstract: Inaccurately labeled training data, or "label noise", poses a significant threat to the integrity of supervised machine learning models.