A Waveform-Based Graph Neural Network Approach for Microseismic Monitoring

In this work, we introduce HEIMDALL, a grapH-based sEIsMic Detector And Locator specifically designed for microseismic applications. Building on recent progress in deep learning (DL), HEIMDALL employs spatiotemporal graph-neural networks to detect and locate seismic events in continuous waveforms. It simultaneously associates and provides preliminary locations by leveraging the output probability functions of the graph-neural network over a dense, three-dimensional grid (0.1 km spacing). By integrating detection and location within a single framework, HEIMDALL aims to address persistent challenges in microseismic data analysis, such as accurately associating wavefront arrivals and enabling consistent and robust event localization in complex geothermal regions. To train our models, we utilize synthetics generated using Green’s function available in the area, in combination with a small fine-tuning over a subset of real data. This approach allows us to achieve homogeneous coverage of the study area while addressing nuances that inevitably arise across synthetic and real domains.

Our evaluation focuses on data collected at the Hengill Geothermal Field in Iceland as part of the COSEIMIQ project (December 2018 to August 2021). Specifically, we analyzed one month of continuous seismic recordings from December 2018 and a brief sequence on February 3, 2019, which occurred in the middle of the geothermal plant. The dataset also features frequent burst sequences, providing an ideal testbed for advanced detection and location algorithms. By benchmarking HEIMDALL against multiple approaches, we reveal both the strengths and limitations inherent in our novel method and in more conventional workflows used in observational seismology.

Ultimately, we highlight the importance of continued innovation in ML-based workflows for induced seismicity monitoring at enhanced geothermal system (EGS) sites, where the capacity to detect and accurately locate a large number of microseismic events can be critical for operational safety and resource management.