# Structural Augmentation in Seismic Data for Fault Prediction
## 5 Conclusions  
In this study, we describe a method called structural data augmentation to obtain diverse field-like seismic data. Many unique virtual structures can be generated in the seismic data by randomly combining multiple parameters. The purpose of our work is to bridge the gap between training data and field data, enhance the generalization ability of seismic data, and obtain corresponding accurate labels to train neural networks. Compared with manual interpretation data learning, synthetic data learning, and transfer learning methods, our approach (1) combines information from theoretical models and knowledge of manual interpretation to provide training data with high generalization capability, (2) bridges the gap between training data and field seismic data, and (3) achieves better results with only a simple U-net. These comparisons illustrate that our method is learning-friendly and may have the prospect of broad industrial application. The fault identification results from field data prove that the proposed method can be effectively applied to seismic data interpretation, and learning sets similar to field data and rich in geological information can improve the applicability of machine learning. Although we emphasize the flexibility and generalization ability of our workflow in seismic data augmentation throughout the paper, this method still has limitations. The structural data augmentation method does not increase the diversity of seismic waveforms and noise types, which may lead to overfitting. Incorporating more types of seismic noise and utilizing different waveform decomposition methods in future work may further \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fault Detection Methods** & **Ours** & **Net\_manual** & **Net\_transfer** & **Net\_syn\_our** & **Net\_syn\_Wu** & **Coherence** & **Curvature** \\  & & [62] & [118] & **Net\_syn\_our** & **[68]** & **[19]** & **[25]** \\ \hline Ours & 1.0 & **0.2558** & **0.1804** & 0.2825 & **0.2870** & 0.2518 & 0.0931 \\ Net\_manual [62] & 0.2888 & 1.0 & 0.1145 & 0.1315 & 0.2101 & 0.1054 & 0.0432 \\ Net\_transfer [118] & 0.1804 & 0.1145 & 1.0 & **0.4418** & 0.1711 & 0.1287 & 0.0599 \\ Net\_syn\_our & 0.2825 & 0.1315 & **0.4418** & 1.0 & 0.2899 & **0.3133** & **0.1142** \\ Net\_syn\_Wu [68] & **0.2870** & 0.2101 & 0.1711 & 0.2389 & 1.0 & 0.2073 & 0.0781 \\ Coherence [19] & 0.2518 & 0.1054 & 0.1287 & **0.3133** & 0.2073 & 1.0 & **0.1782** \\ Curvature [25] & 0.0931 & 0.0432 & 0.0599 & 0.1142 & 0.0781 & **0.1782** & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results Comparison Test with F1 Score in Work Area A. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Fault Detection Methods** & **Ours** & **Net\_manual** & **Net\_transfer** & **Net\_syn\_our** & **Net\_syn\_Wu** & **Coherence** & **Curvature** \\  & & [62] & [118] & **Net\_syn\_our** & **[68]** & **[19]** & **[25]** \\ \hline Ours & 1.0 & **0.2008** & **0.1772** & **0.3372** & **0.2125** & **0.3221** & **0.2030** \\ Net\_manual [62] & 0.2008 & 1.0 & 0.1175 & 0.1398 & 0.1842 & 0.1267 & 0.0571 \\ Net\_transfer [118] & 0.1772 & 0.1175 & 1.0 & **0.4335** & 0.1381 & 0.1299 & 0.0703 \\ Net\_syn\_our & **0.3372** & 0.1398 & **0.4335** & 1.0 & 0.1859 & 0.2984 & 0.1956 \\ Net\_syn\_Wu [68] & 0.2125 & 0.1842 & 0.1381 & 0.1859 & 1.0 & 0.1576 & 0.0929 \\ Coherence [19] & 0.3221 & 0.1267 & 0.1299 & 0.2984 & 0.1576 & 1.0 & **0.4259** \\ Curvature [25] & 0.2030 & 0.0571 & 0.0703 & 0.1956 & 0.0929 & **0.4259** & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results Comparison Test with F1 Score in Work Area B.