Geologic fault detection at high precision and resolution is the key for fine structure and reservoir modeling. Previous studies using neural networks for fault segmentation mainly focus on the local features of the targets and train the networks using synthetic data sets. To increase the fault segmentation resolution only using a limited amount of seismic field data, we develop an adversarial neural network architecture for high-resolution identification of faults (FaultAdvNet) taking advantage of global feature fusion. The architecture consists of (1) a light-weight segmentation module (approximately 0.49 M parameters), (2) a feature fusion module considering reflectors of faults and surrounding stratums, and (3) a discriminator module acting as a regularization term. Case studies using seismic field data from the Gulf of Mexico show an overwhelming performance improvement of the FaultAdvNet when compared with other fault detection methods. The FaultAdvNet picks all of the faults with sufficiently high confidence and low prediction risk. The predicted faults of the FaultAdvNet have good continuity and show clear boundary with fault probability values mainly ranging from 0.95 to 1. Saliency analysis also suggests that the FaultAdvNet can focus on the target at a sufficiently higher resolution (dozens of meters). Functionality experiments verify the mechanisms of the feature fusion module and the discriminator module in FaultAdvNet. We consider that a neural network (such as the discriminator) can serve as a data-driven regularization term to constrain the target network (the segmentation network) efficiently, especially given a limited amount of seismic data.

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