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

TL;DR

This paper introduces HEIMDALL, a graph neural network-based deep learning model for microseismic monitoring that improves event detection accuracy and reduces manual effort, aiding geothermal energy projects.

Contribution

The paper presents a novel end-to-end graph neural network approach for simultaneous seismic phase picking, association, and event location, optimized for real-time monitoring.

Findings

Significantly increased detection of microseismic events compared to previous systems.

Reduced false event detection and manual oversight.

Validated effectiveness in geothermal seismic monitoring in Iceland.

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.