# Detection of Rock Mass Defects Using Seismic Tomography
## 6 Conclusions  
This study proposed the use of wave velocity tomography to identify abnormal structures and determine the boundaries of abnormal areas. The method was tested on both non-empty and empty areas, and the accuracy of the obtained velocity images was quantified. The method's boundary recognition reliability was also analyzed. The conclusions are as follows:  
1. Twelve tests were conducted for four geometric dimensions multiplied by three velocity structures. The inversion of the velocity images showed that the inversion accuracy was significantly higher in normal areas than in abnormal areas. The size of the estimated anomalous structure in the prior model has a significant impact on the accuracy of the results. The larger the wave velocity difference between the anomalous and normal regions, the lower the inversion accuracy of the velocity image. 2. By calculating the number of intersections of ray paths in nodes and identifying boundaries based on their density, it was determined that when the actual speed difference is small and the shape in the prior model is inaccurate, the identified boundary error is significantly larger. Among the 12 test results, only three of them had boundary recognition results with accuracy rates below 90%. 3. Due to the unknown existence of abnormal areas in practical engineering contexts, we used a completely homogeneous prior model. The results showed that although the inversion velocity may not be accurate, it is sufficient to identify boundaries. The larger the velocity difference in the original model, the clearer the recognized boundary. 4. Tests considering circle, square, and triangle areas showed that when the abnormal area is empty, the rounder the abnormal area, the higher the accuracy of the velocity  
Figure 17: Data distribution chart of the goaf detection area’s mean relative error, standard deviation of relative error, and percentage of high-accuracy results across the synthetic experiment and field experiment (both the entire region and different sections). field inversion and the easier it is to identify the boundary; the larger the edge angle of the abnormal area, the lower the inversion accuracy of the velocity field, and the more difficult it is to clearly describe the boundary. 5. Physical detection of concealed goafs in the tantalum-niobium mine belonging to the Hunan Fuguihengton Mining Company was conducted through wave velocity imaging which, when integrated with historical statistical data regarding goat occurrence in each section of the mine, revealed a total of 112 identified or suspected goafs. Of these, 100 were confirmed as actual goafs and 12 remained suspected goafs. This finding underscores the efficacy of the proposed method for detecting empty anomalous areas, particularly in accurately identifying and delineating the boundaries of hidden goafs. 6. By conducting error analysis on the goat detection results obtained from synthetic and field experiments, and comparing the mean relative error, standard deviation, and the percentage of high-accuracy results across different scenarios, it is observed that the error values of goat detection remain low and stable at both the experimental scale and the practical engineering scale. This demonstrates that the proposed method for detecting anomalous rock mass regions maintains high accuracy in practical applications. However, the detection accuracy in Section 2 is suboptimal, which is likely due to the irregular shape, large edge angles, and scattered distribution of the goafs in this section. 7. Through both simulated experiments and field applications, this study demonstrates that the proposed method of using wave velocity tomography to identify anomalous structures offers advantages such as fast detection, wide coverage, and high accuracy.