# Structural Augmentation in Seismic Data for Fault Prediction
## 4 Results
As shown in Figure 11e, the results of Net_syn_Wu not only contain misinformation, but also the main fault is discontinuous (e.g., the green rectangular area in Figure 11e). This result shows that the geological information accommodated by the theoretical structural models is limited and can seriously affect the effectiveness of machine learning. The richer the geological information contained in the training data, the stronger the applicability of the trained network. Coherence and curvature are severely affected by noise when dealing with such ultra-deep complex seismic data (Figure 11g,h). Figure 12 demonstrates the 3D full view and 2D slice details of the fault identification results in work area C. In work area C, both our structural data augmentation method and Net_manual extract some misinformation (Figure 12b,c). However, the extraction of secondary faults by Net_manual is not as good as our structural data augmentation method (e.g., the green rectangular regions in Figure 12c). These errors indicate that only using conventional augmentation methods from computer vision is insufficient to train the neural network effectively for fault extraction. As shown in Figure 12d,f, Net_transfer effectively reduces some misinformation in the results of Net_syn_our. However, transfer learning also undermines the integrity of the main fault extraction (e.g., the green rectangular area in Figure 12d). As shown in Figure 12e, Net_syn_Wu yields good fault identification results but simultaneously extracts partial stratigraphic information (e.g., the green rectangular area in Figure 12e). Coherence and curvature highlight a portion of the fault network in the identification results of the field data but are severely affected by noise. Figure 11: Fault recognition results of work area B. (**a**) Field seismic data. The yellow line represents the position of the detailed slice. (**b**) Detected faults by our structural data augmentation method. (**c**) Detected faults by Net_manual [62]. (**d**) Detected faults by Net_transfer [118]. (**e**) Detected faults by Net_syn_Wu [68]. (**f**) Detected faults by Net_syn_our. (**g**) Detected faults by Coherence [19]. (**h**) Detected faults by Curvature [25]. To further quantitatively evaluate the fault detection accuracy, we use results comparison tests [119] between the fault results extracted by different methods in work areas A, B, and C. Since the ground-truth fault behind these field seismic signals is unknown, and since each fault result contains false-positive and false-negative results due to different training sets and algorithms, it is a challenging task to evaluate the validity of different fault identification results accurately. Results comparison tests calculate relative performance metrics for other fault extraction results by assuming that one fault extraction result is ground truth. Taking the first row of the result comparison test as an example, the F1 scores of each method are calculated by setting our method as ground truth. Tables 1-3 show the F1 score of the results comparison tests in the work areas A, B, and C, respectively. From the results comparison tests, it can be observed that the results comparison coefficients between Net_syn_our and Net_transfer and between coherence and curvature are generally large. This correlation may indicate that the two groups of methods are algorithmically similar. Despite this unfavorable condition, our structural data augmentation method still achieves the highest scores in many cases, which means that most of the faults extracted by our method are also present in the results of other methods and are more likely to be real faults. Figure 12: Fault recognition results of work area C. (**a**) Field seismic data. The yellow line represents the position of the detailed slice. (**b**) Detected faults by our structural data augmentation method. (**c**) Detected faults by Net_manual [62]. (**d**) Detected faults by Net_transfer [118]. (**e**) Detected faults by Net_syn_Wu [68]. (**f**) Detected faults by Net_syn_our.