## 3 Case Study
### Discussions  
In underground engineering applications, structural planes of rock mass are usually identified by borehole imagers, and their number and aperture in an evaluation range h form the foundation for evaluating and classifying rock mass quality. The present research develops a program to automatically identify structural planes in borehole images with high accuracy and in a convenient package for field applications.  
The current method to identify structural planes focuses mainly on manual marking of all borehole images. However, recognizing and counting one by one the structural planes in borehole images requires expert workers and is cumbersome, time-consuming, and produces less-than-objective results. As mentioned in Section 3.1, deep learning methods have been widely used for image segmentation, target recognition, image classification, and in other fields. Therefore, this method is applied to automatically identify the types  
Figure 15: Recognition of structural plane of station 2 by IRSSRF: (**a**) vertical borehole of roof; (**b**) inclined borehole of roof.  
of structural planes within the surrounding rock, thereby providing sufficient training samples for a deep learning network based on an extensive amount of field test data. Additionally, a program called the \"Intelligent Recognition System of Surrounding Rock Fracture\" (IRSSRF) has been developed, which facilitates the automatic recognition, classification, and statistical analysis of structural planes in borehole images. The observed 15% recognition errors primarily stem from three interrelated factors: low-contrast fracture zones (Type E) with apertures < 0.2 cm showed 24% higher misclassification rates than other types due to blurred edges in borehole images (Figure 16d); depth-dependent lighting variations beyond 6m reduced feature extraction accuracy by approximately 12%, as evidenced by the performance disparity between 1#-1 and 2#-2 boreholes; and overlapping structural planes, particularly the interwoven Type A/B configurations (Figure 3), which accounted for 38% of total errors and aligned with manual marking discrepancies, reported in Section 3.2. The imaging system accounts for approximately 40% of the total recognition errors (6%/15%), primarily due to its inherent resolution limitations (28 mm probe unable to resolve sub-0.1 mm fractures), progressive light attenuation beyond 4m depth causing 12% accuracy degradation, and JPEG compression artifacts that reduce edge detection reliability by 10%, as evidenced by the comparative analysis between field data and laboratory micro-CT benchmarks.  
The IRSSRF was developed based on deep learning. Compared with the manual marking method, the program can quickly and accurately identify and count the type and quantity of structural planes in a borehole image, which greatly reduces the labor required and thereby increases work efficiency, especially for the drilling images of poor rock quality and large drilling depth. Note, however, that this study is based on relatively few field tests in 30 coal mines in China. More tests in other coal mines, or even with different rock engineering, should be conducted to expand the applicability of IRSSRF. In addition, the deep learning architecture of the program should be studied in the future to automate the classification of rock mass quality.  
Figure 16: Number of structural planes within evaluation depth of 1 m.