# Structural Augmentation in Seismic Data for Fault Prediction
## 4 Results  
In this section, we demonstrate the applicability of our structural data augmentation method using field seismic data obtained from three different work areas in the Tarim Basin, China (work areas A, B, and C). Affected by the deep geological environment, the field data selected from the ultra-deep section (over 7000 m depth) contain complex structural features. In particular, multi-stage and multi-scale strike-slip faults are developed in these ultra-deep strata, which are difficult to characterize effectively by conventional methods [8]. We compare our fault prediction results with three recently popular machine  
Figure 9: Network training based on (**a**) manual interpretation data, (**b**) synthetic data, (**c**) transfer learning, and (**d**) structural data augmentation. learning methods (manually interpreted data learning [62], synthetic data learning [68], and transfer learning [118]) and two classical conventional methods (coherence method [19] and curvature method [25]). Figure 10 demonstrates the 3D full view and 2D slice details of the fault identification results in work area A. In work area A, our structural data augmentation method and Net_manual perform well, especially for flower-like strike-slip fault structures in detailed slices (Figure 10b,c). A few imperfections in the Net_manual may be due to the bias of the hand interpretation (e.g., the green rectangle in Figure 10c). The performance of Net_transfer (Figure 10d) is improved compared to that of Net_syn_our (Figure 10f). Transfer learning effectively reduces lineaments in the results that may not be related to faults (e.g., the green rectangular area in Figure 10d,f). However, the fault recognition results obtained by Net_transfer are still imperfect. Both Net_syn_Wu and Net_syn_our extract misinformation unrelated to faults (Figure 10e,f). This misinformation indicates that the synthetic data contain only limited ideal geological information and cannot fully bridge the gap between synthetic data and field seismic data. However, it is difficult for Net_syn_Wu to extract complete faults (e.g., the flower structure in the green rectangular area in Figure 10e). These incomplete fault characterization results may stem from the fact that some geological features have been simplified or omitted in the conventional structural modeling framework (especially for strike-slip faults). Coherence and curvature can produce relatively complex fault network images, but they are still disturbed by noise and makes it difficult to identify the fault structure completely (Figure 10g,h). Figure 11 demonstrate the 3D full view and 2D slice details of the fault identification results in work area B. As shown in Figure 11b, our structural data augmentation method can effectively identify major faults. However, the results also contain some errors that may not be related to the fault (e.g., the green rectangular area in Figure 11b). These errors may be due to the differences between different work areas, indicating that our method  
Figure 10: Fault recognition results of work area A. (**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]. may still be imperfect in generalization ability. As shown in the green rectangular area in Figure 11c, Net_manual cannot extract complete faults. This incomplete result illustrates that a network trained with only the interpreted data from one work area may produce errors in cross-work-zone applications. As shown in Figure 11d,f, although the transfer learning method can reduce the error while preserving the main fault, the effect of fine-tuning is limited, and the result still has some misinformation (e.g., the green rectangular area in Figure 11d,f).