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

2025-07-14

AI Summary

In geothermal microseismic monitoring, conventional pipelines suffer from high false-alarm rates, heavy reliance on manual intervention, and poor model generalizability due to the decoupled treatment of phase picking, event association, and source localization. To address these limitations, this paper proposes an end-to-end graph neural network (GNN)-based joint modeling framework. Seismic stations are modeled as graph nodes, and their spatiotemporal dependencies are leveraged to simultaneously perform detection, association, and localization—enabling both retrospective analysis and near-real-time monitoring. The approach innovatively integrates graph theory with deep learning and employs a sliding-window mechanism to minimize hyperparameter tuning and reduce dependency on transfer learning. Evaluated on field data from the Hengill geothermal area in Iceland, the method achieves significantly higher detection rates for the 2018 Mw4 sequence and the 2019 dense low-magnitude swarm compared to state-of-the-art automated systems, while substantially lowering false-positive rates and markedly improving sensitivity to small-magnitude events.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Detects and locates microseismicity using graph neural networks
• Improves event detection in geothermal seismic regions
• Reduces false events and manual oversight in monitoring

Innovation

• Graph neural networks for seismic monitoring
• End-to-end pipeline for catalog creation
• Reduces false events and manual oversight