HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity

• URL: http://arxiv.org/abs/2507.10850v1
• Date: Mon, 14 Jul 2025 22:47:49 GMT
• Title: HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
• Authors: Matteo Bagagli, Francesco Grigoli, Davide Bacciu,
• Abstract summary: We present a new deep-learning model that forms an end-to-end pipeline for seismic catalog creation.<n>It was tested in the complex geothermal area of Iceland's Hengill region.<n>Results showed a significant increase in event detection compared to previously published automatic systems.
• Score: 13.628458744188325
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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