  The carbon capture, utilization, and storage (CCUS) framework is an essential
component in reducing greenhouse gas emissions, with its success hinging on the
comprehensive knowledge of subsurface geology and geomechanics. Passive seismic
event relocation and fault detection serve as indispensable tools, offering
vital insights into subsurface structures and fluid migration pathways.
Accurate identification and localization of seismic events, however, face
significant challenges, including the necessity for high-quality seismic data
and advanced computational methods. To address these challenges, we introduce a
novel deep learning method, DeFault, specifically designed for passive seismic
source relocation and fault delineating for passive seismic monitoring
projects. By leveraging data domain-adaptation, DeFault allows us to train a
neural network with labeled synthetic data and apply it directly to field data.
Using DeFault, the passive seismic sources are automatically clustered based on
their recording time and spatial locations, and subsequently, faults and
fractures are delineated accordingly. We demonstrate the efficacy of DeFault on
a field case study involving CO2 injection related microseismic data from the
Decatur, Illinois area. Our approach accurately and efficiently relocated
passive seismic events, identified faults and aided in the prevention of
potential geological hazards. Our results highlight the potential of DeFault as
a valuable tool for passive seismic monitoring, emphasizing its role in
ensuring CCUS project safety. This research bolsters the understanding of
subsurface characterization in CCUS, illustrating machine learning's capacity
to refine these methods. Ultimately, our work bear significant implications for
CCUS technology deployment, an essential strategy in combating climate change.
