Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images

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Deep-learning tomography (2018, background): “…2017) are paving inroads in different aspects of the exploration workflow, but most work is still focused on identifying features or attributes in migrated images (Hale, 2012; Hale, 2013; Guillen, 2015; Addison, 2016; Bougher and Herrmann, 2016), therefore helping to tackle the interpretation step.”
Directional structure-tensor-based coherence to detect seismic faults and channels (2017, unknown): “By applying fault-oriented smoothing to the numerators and denominators of semblance ratios, a semblance image can be further enhanced for fault surface extraction (Hale, 2013; Wu and Hale, 2016; Wu et al., 2016).”
FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation (2019, background): “This means that measuring seismic reflection continuity or discontinuity alone is insufficient to detect faults (Hale, 2013).”