# Structural Augmentation in Seismic Data for Fault Prediction
## 2 Methods  
When training a deep learning network to detect fault structures in 3D seismic data, numerous field-like 3D seismic volumes are required as inputs, and the corresponding 3D fault structures are required as labels. It is not difficult to obtain numerous seismic volumes by augmenting field data with existing data augmentation techniques derived from computer vision [62; 79; 80]. However, to effectively improve the training performance, the applied data augmentation techniques cannot change the semantics of the data [94]. Conventional data augmentation methods, such as flipping [81], random brightness [86], random drop [87], and grid distortion [62], may lead to stratigraphic overturn and tectonic distortion, which can produce geologically unacceptable data samples. Therefore, some of these data augmentation algorithms may only provide limited assistance for fault detection.  
To cope with the multi-stage and multi-scale complex structures developed in ultra-deep seismic data, the proposed augmentation workflow increases data diversity by generating various virtual structures containing multi-scale folds, listric faults, oblique-slip displacement fields, and multi-directional fault drags. The basic idea of this augmentation method is to add virtual faults and folds into the seismic data to ensure that the data remain reasonable in the geological sense. For structural geology, this process can be considered as the development of folds or faults due to multi-stage tectonic events. Randomly adding various folds and faults into the 3D field seismic data also increases the diversity and generalization ability of the samples. Figure 1 demonstrates an example of adding a series of virtual structures into the field seismic data and manual fault interpretation. The detailed workflow of the structural data augmentation is as follows:  
(1) As shown in Figure 1a,d, sub-volumes (128 \(\times\) 128 \(\times\) 128 in this study) are truncated from a 3D field seismic data volume and corresponding manually interpreted volume.  
(2) Generate virtual folds. As shown in Figure 1b, the process of adding virtual folds can be simulated by raising and lowering the data points. The randomly generated folds determine the shift distance of each data point.  
(3) Generate virtual faults. As shown in Figure 1c, the process of adding virtual faults can be simulated by making discontinuities on both sides of virtual fault surfaces.  
The randomly generated fault surfaces determine the specific location and basic shape of faults. The randomly simulated fault near-field displacements and fault drags determine the direction and magnitude of the discontinuities on both sides of the faults. To further increase the generalization ability of the dataset and the anti-noise ability of fault identification, a small amount of random noise is also added into the seismic data (\(SNR=[4,5]\) in this study).  
(4) Repeat steps 2 and 3 until the generated samples are sufficient to train a deep learning network model.  
Different from conventional data generation workflows [66, 67, 44, 68], the proposed structural data augmentation workflow can automatically generate various datasets containing multi-scale folds, listric faults, and oblique-slip displacement fields to provide a variety of data-label pairs to train neural networks.