# Fault Imaging of Seismic Data Based on a Modified U-Net with Dilated Convolution
## 6 Application  
This paper used field data to verify the effectiveness of the trained network, and the fault probability volume is shown in Figure 9. In order to facilitate the interpretation of results, the opacity of the fault probability cube was adjusted and superimposed on the original seismic image. At the same time, we used a trained U-Net to identify faults of this data, and other parameters were completely the same except for GCM and GSM modules. The study area is located in a sandstone oil field in China, and the faults are mainly Y-shaped throughout the section and occur in almost every formation [29]. In the 700 ms-1500 ms time window, the number of faults is the largest, and the characteristics of faults are the most complex [30]. As depth increases, the imaging accuracy of seismic data decreases and the difficulty of fault imaging becomes more and more. On the plane, the fault is affected by the tension and strike-slip stress mechanism, and the fault direction is mainly NE and NW. This data set consists of 495 (lines) \(\times\) 580 (CDPs) with a CDP spacing of 25 m and a line spacing of 25 m. The data are sampled at 1 ms with a length of 2 s. The inline number and xline number are in the range of (2410,2905) and (3600,4180), respectively. By using an NVIDIA TITAN Xp GPU, it takes about 110 min to calculate the fault probability volume. The randomly selected vertical profiles and time slice are inline 2510, xline 4025 and time slice at 1540 ms, respectively. The fault imaging results of the modified U-Net and U-Net are shown in Figures 10-12.  
Figure 8: (**a**) The training and validation accuracy both will increase with epochs, whereas (**b**) the training and validation loss decreases with epochs.  
Figure 7: (**a**) Seismic data sets and (**b**) their fault labels.  
Figure 11: (**a**) A seismic image is displayed with faults that are imaged using **(b)** the trained U-Net model and **(c)** the trained modified U-Net model.  
Figure 10: Three seismic images are displayed with faults that are imaged using the trained modified U-Net model. (**a**) Inline 2510; (**b**) Xline 4025; (**c**) a time slice at 1540 ms.  
Figure 9: (**a**) Original 3D seismic data volume and (**b**) its fault probability volume.  
Figure 10 represents three seismic images in different directions with different scales faults that are imaged using the trained modified U-Net model. Figure 11b shows the fault image predicted by the trained U-Net model and Figure 11c shows the modified U-Net prediction results. The U-Net result (Figure 11b) is reliable enough to depict faults in this seismic image, however, much of the detail is still missing compared to features predicted by the modified U-Net (Figure 11c). Figure 12b,c illustrate fault imaging results of different slices. We observe that most faults can be clearly imaged under the trained modified U-Net model, and multiple groups of faults in different directions can be distinguished on horizontal slices. Figure 12b is the result of U-Net prediction, some small fracture information has not been portrayed. In summary, the field data example shows that the proposed method based on the modified U-Net has superior performance in detecting faults of multiple scales, and provides relatively high sensitivity and continuity.