HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
This provides a robust monitoring tool for geothermal seismic regions, enhancing operational risk mitigation during geothermal energy exploitation, though it is incremental as it builds on existing graph-based methods.
The paper tackled microseismicity monitoring by developing a deep-learning model using graph neural networks to detect and locate seismic events, showing a significant increase in event detection compared to previous systems, including specific sequences like a $4 M_w$ event in December 2018.
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.