arXiv:2606. 10868v1 Announce Type: new Abstract: Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as a causal model is fed its own outputs over hundreds of steps, small per-step errors compound into phase drift that pointwise metrics fail to detect.
By Waleed Esmail, Stuart Russell, Jana Klinge, Alexander Kappes, Christine Thomas
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...
By Quenton Yeo, Zhaoge Bi, Linghan Huang, Luke Stephen Higgins, Flora Salim, Huaming Chen
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:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.
By Tao Han, Zhibin Wen, Zhenghao Chen, Dazhao Du, Song Guo, Lei Bai
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)
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.
By Wei Quan, Denise Gorse
The paper presents a framework that adapts general-purpose Vision Foundation Models (VFMs) to seismic data denoising using Parameter‑Efficient Fine‑Tuning with Low‑Rank Adaptation (LoRA). It introduces a kurtosis‑guided unsupervised test‑time adaptation module that updates only LoRA parameters to self‑calibrate for site‑specific noise without ground truth. Experiments on exploration seismic images and DAS data demonstrate that the approach matches or surpasses domain‑specific models and generalizes well to unseen cross‑site data.
By Jiahua Zhao, Umair bin Waheed, Jing Sun, Yang Cui, Nikos Savva, Eric Verschuur
arXiv:2607. 04440v1 Announce Type: cross Abstract: Seismic wavefield simulation is fundamental to seismology, but conventional finite-difference (FD) methods remain limited by numerical dispersion and stability constraints, which often require dense spatial grids and small time steps and thereby severely limit the effectiveness of iterative inversion workflows.
By Shijun Cheng, Tariq Alkhalifah
arXiv:2609.36193v1 Announce Type: new
Abstract: Learning from scientific measurements often requires aligning modalities with different spatial support and resolution. Subsurface characterization is...
By Meher Gajula, Keyla Gonzalez, Ben Lasscock, Alejandro Valenciano
arXiv:2606. 25439v1 Announce Type: new Abstract: Deep learning-based models have achieved state-of-the-art performance in Time Series Forecasting (TSF), yet their evaluation remains dominated by pointwise error metrics such as Mean Squared Error (MSE), which quantify numerical accuracy but overlook structural properties of the forecast signal, including recurrent dynamics, oscillatory behavior, and phase alignment.
By Sandeepa Weerasekara, Sandareka Wickramanayake
arXiv:2607. 16080v1 Announce Type: new Abstract: Precipitation nowcasting over the immediate 10-90 min period is important for flood management and real-time decision-making in urban regions.
By Akshay Sunil, Muhammed Rashid, Raja Sekhar Sivaraju, Sushma Nair, Subimal Ghosh
arXiv:2606. 19026v1 Announce Type: cross Abstract: Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena.
By David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia, Nick P. Bassill, Chris D. Thorncroft