{"ID":2895094,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.10850","arxiv_id":"2507.10850","title":"HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity","abstract":"In this work, we present a new deep-learning model for microseismicity monitoring that utilizes continuous spatiotemporal relationships between seismic station recordings, forming an end-to-end pipeline for seismic catalog creation. It employs graph theory and state-of-the-art graph neural network architectures to perform phase picking, association, and event location simultaneously over rolling windows, making it suitable for both playback and near-real-time monitoring. As part of the global strategy to reduce carbon emissions within the broader context of a green-energy transition, there has been growing interest in exploiting enhanced geothermal systems. Tested in the complex geothermal area of Iceland's Hengill region using open-access data from a temporary experiment, our model was trained and validated using both manually revised and automatic seismic catalogs. Results showed a significant increase in event detection compared to previously published automatic systems and reference catalogs, including a $4 M_w$ seismic sequence in December 2018 and a single-day sequence in February 2019. Our method reduces false events, minimizes manual oversight, and decreases the need for extensive tuning of pipelines or transfer learning of deep-learning models. Overall, it validates a robust monitoring tool for geothermal seismic regions, complementing existing systems and enhancing operational risk mitigation during geothermal energy exploitation.","short_abstract":"In this work, we present a new deep-learning model for microseismicity monitoring that utilizes continuous spatiotemporal relationships between seismic station recordings, forming an end-to-end pipeline for seismic catalog creation. It employs graph theory and state-of-the-art graph neural network architectures to perf...","url_abs":"https://arxiv.org/abs/2507.10850","url_pdf":"https://arxiv.org/pdf/2507.10850v1","authors":"[\"Matteo Bagagli\",\"Francesco Grigoli\",\"Davide Bacciu\"]","published":"2025-07-14T22:47:49Z","proceeding":"physics.geo-ph","tasks":"[\"physics.geo-ph\",\"cs.LG\"]","methods":"[\"Graph Neural Network\"]","has_code":false}