# Structural Augmentation in Seismic Data for Fault Prediction
## 1 Introduction
However, the confidentiality and scarcity of field and label data often hinder the application of machine learning methods [51, 52, 64]. Therefore, the second type of methods use synthetic seismic data and theoretical fault labels to train the neural network [44, 65, 66, 67, 68, 69, 70, 71, 72, 73]. These methods effectively alleviate the problem of training datasets scarcity and excel in some seismic data applications. However, the geological information and waveform features contained in synthetic data are usually ideal and bounded. It is difficult to fully bridge the gap between synthetic and field data using only theoretical models [62, 74]. The third category of methods utilizes the idea of transfer learning to identify faults [74, 75, 76, 77, 78]. A small amount of field seismic data and manually interpreted labels are used to fine-tune the neural network pre-trained from synthetic data. While this fine-tuning technique can improve the performance of fault detection, the results are not the best in some field data applications [62]. In summary, the current popular machine-learning-based fault detection methods may all face the problem of insufficient data and information. One of the biggest challenges for machine learning in fault identification and even in geosciences is the lack of numerous, informative, and field-like training data [51, 52]. Some classic data augmentation methods can help alleviate this difficulty [62, 79, 80], such as flipping [81], rotating [82], cropping [83], intensity transformation [84], down-sampling [85], random brightness [86], grid distortion [62], and random drop [87]. However, some of these data augmentation methods may not meet the geological constraints when applied directly to seismic data. Therefore, we attempt to bridge the gap between the training set and the field seismic data to improve the fault characterization capability in this paper. In this paper, the characterization of fault structures in 3D seismic data is considered as an image segmentation problem, which is solved by a simple 3D U-net. The architecture of the 3D U-net is improved from the 2D U-net used in the medical image field [88]. Although there are many networks with more parameters and more complex architectures now [89, 90, 91, 92, 93], we still only apply a simple U-shaped network in this study. The simple 3 DUnet network architecture is used because 3D U-net has been validated in fault detection tasks [44; 66; 70] and because we want to put more emphasis on the impact of the data on the results and improve computational efficiency. To alleviate the problem of insufficient labeled field-like datasets in machine learning methods, we propose a structural data augmentation method in this study. This method creates virtual geometric folds and faults in field seismic signals and interpretation data to obtain semi-real-semi-synthetic seismic data and corresponding labels. Compared to methods that train networks using field data and manually interpreted data, our approach allows training datasets to contain not only the experience of geologists but also the knowledge of theoretical models. In addition to improving the generalization capability, our method requires interpretation for only a small amount of field data, which avoids the tedious work of completely labeling large seismic data. Compared to synthetic data learning and transfer learning methods, our method is applied directly to field data, allowing our approach to be more effective in bridging the difference between the training set and field data. Through our structural data augmentation framework, we automatically generate 200 data-label pairs. After further rotation augmentation, these data sets effectively train our neural network to identify fault structures in 3D seismic signals.