Synthesizing a realistic seismic training data set incorporating prior geologic patterns and seismic imaging features for supervised convolutional neural network-based intracratonic strike-slip fault detection

Intracratonic strike-slip faults play a vital role in hydrocarbon migration and accumulation, making their accurate detection crucial for subsurface structure interpretation and reservoir characterization. Although numerous approaches have been developed for automatic fault interpretation, they are still unable to fully recognize intracratonic strike-slip faults with subtle reflection variations in seismic images. We develop a supervised convolutional neural network (CNN) to automatically and precisely delineate these faults in 3D seismic images. The primary obstacle in using a CNN for this task is the lack of diverse seismic training samples and corresponding high-quality labels. To address this, we develop an efficient two-step workflow for automatically generating a tremendous amount of 3D seismic training sample pairs: (1) guided by the prior geologic structure patterns of the study area, we use parameterized vector fields to simulate various velocity models with accurate fault labels incorporating realistic intracratonic strike-slip faulting features, such as the transtensional, transpressional, and strike-slip translational types and (2) following seismic imaging laws, we synthesize seismic training samples from simulated velocity models through the point-spread function-based convolution method. One synthetic and two field examples demonstrate that the CNN trained with this seismic data set containing prior geologic patterns and seismic imaging features outperforms the conventional fault detection approaches in accurately and continuously delineating intracratonic strike-slip faults.