Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization from geophysical data have been one of the center challenges in geophysics and seismology as it holds the key to understanding subsurface dynamics ranging from borehole, reservoir, to regional scales. While paradigms of auto or semi-auto fault delineation either based on seismicity location analysis or on seismic migration image reflector discontinuity identification have been well established, a systematic method that can integrate both seismic image and seismicity location information is still missing. We develop a novel machine learning (ML) model that integrates seismic reflector image and seismicity location information into a unified model to automatically identify geological faults and characterize their geometrical properties. We detail the architecture of this neural network, the strategy and procedure of high-quality training data-label generation, as well as the validation results on the trained models. Specially, we also use two field data examples to validate the efficacy and accuracy of our ML model. The results demonstrate that by integrating seismicity location information and seismic migration image in a unified framework, the end-to-end neural network provides notably higher fidelity in delineating subsurface faults and its geometrical properties compared with image-only fault detection methods.