ML-aided induced seismicity processing and interpretation for enhanced geothermal systems

Abstract The increasing demand for sustainable energy has intensified interest in geothermal energy, with enhanced geothermal systems (EGS) emerging as a key solution. Hydraulic fracturing, a critical technique in EGS, often induces microseismic events that provide valuable insights into subsurface fracture mechanisms and reservoir properties. This study examines the promising and evolving role of machine learning (ML) in analyzing induced seismicity in geothermal fields, focusing on applications including data acquisition, processing, and interpretation. Specifically, we present four key ML applications: wavefield inpainting for reconstructing missing seismic data, generative artificial intelligence for wavefield simulation, transformer-based phase picking for detecting small seismic events, and clustering methods to analyze the spatiotemporal evolution of seismicity. These approaches enhance the efficiency and accuracy of seismic monitoring, offering innovative tools for optimizing geothermal energy extraction. Additionally, the study highlights the importance of preprocessing and data augmentation in addressing challenges such as data scarcity and noise in seismic data sets. Techniques such as spectral analysis, attenuation correction, noise injection, and the use of multiple time windows are key to obtaining reasonable results from ML models with limited amounts of data. This research provides insights into ML-aided induced seismicity processing and interpretation for seismic monitoring and reservoir management in geothermal fields, contributing to more efficient and sustainable energy operations while mitigating large induced seismic events.