## 4 Case Analysis
### Interpretation of Result  
The image results of the above section effectively highlight the optical image of rock wall texture and rock integrity characteristics. At the same time, combined with the rock response and hole wall profile characteristics in the acoustic scanning, the comprehensive image fusion of various factors is realized and thus provides a more abundant data source for the judgment of the weak interlayer. Before reconstructing the borehole rock wall expansion image, as shown in Figure 13a, and the post-fused image is shown in Figure 13b. As can be seen from Figure 13, the rock wall structural surface in Figure 13a is a more structured surface texture, and the less structured surface texture is shown in Figure 13b. This is mainly due to the multiple types of structural surfaces on the hole wall, and not all structural surfaces are a weak interlayer. Through the technical processing in this paper, the real software structure can be highlighted and the effective screening of information can be realized. Therefore, the structural surface of the hole wall is less informative and more likely to be a weak feature. The image of the borehole rock wall, not processed by the present technique, is shown in Figure 14a. Structural feature regions were identified by fusing synthetic images of the borehole rock wall with multi-source data, and the results are shown in Figure 14b. Through the comparison, it can be seen that to identify the image considering multi-source factors, only the region with color difference can only be identified through the color depth  
Figure 12: Reconstructed image and binary image. (**a**) Fusion image, (**b**) binarized image. Figure 13: Image comparison before and after well wall fusion. (**a**) Optical image (**b**) processed image. information. The results are polyanalytical. At the same time, the target features are not focused, and the specific types of structural features cannot be distinguished. Through this technology, the structural characteristic region containing multiple information can be identified, which can reflect the essential difference characteristics of the rock, and effectively identify the weak and non-weak areas of the rock. Combined with the energy difference characteristics of the image pixels, the contour 3D map of the rock wall image was established, as shown in Figure 15. Figure 15a shows the 3D drawing before the borehole wall fusion, and Figure 15a is the 3D figure after the borehole wall fusion. As can be seen from Figure 15, the 3D contour map surface of the hole wall was not smooth, and the vertical depth jumps in more areas before the fusion technology treatment presented here. After the fusion technique, the 3D contour map surface of the hole wall was smooth, and the longitudinal depth jump area was small. This is mainly due to the fusion treatment; the target areas to be analyzed are more concentrated. Through three-dimensional high treatment, the rock characteristics of rock walls can be presented more intuitively, which can facilitate the division and identification of weak areas. To verify the correctness of this method, the reconstructed image formed by the present method is compared with the traditional method, as shown in Figure 16. The blue curve  
Figure 14: Comparison of recognition before and after hole wall image fusion. (**a**) Optical image recognition, (**b**) fusion hole wall image recognition. Figure 15: 3D images before and after borehole wall fusion. (**a**) 3D images before fusion, (**b**) 3D graphics after fusion. in the figure represents the values obtained by the optical borehole TV, the green curve represents the values obtained by the method of this paper, and the red curve represents the values obtained with the wave speed test technique. To compare the different values, the three data were normalized separately.