# Detection of Rock Mass Defects Using Seismic Tomography
## 4 Application
### Results  
After obtaining the arrival times of seismic waves from the sensors, noise signals are identified and removed by comparing their waveforms with those of various noise sources. Based on the characteristics of downhole noise sources and their waveforms, noise signals such as drilling impact, blasting vibrations, and electrical interference are accurately recognized and eliminated, thereby improving the quality of the signal set. The target area was divided into a grid, with 5 m \(\times\) 5 m grids filling six section planes. Based on the parameter settings from Section 3's synthetic experiment and the specific conditions of the target area, the maximum number of iterations, the residual tolerance to ceasing iteration, and the quasi-Newton step size are set to 20, 1 \(\times\) 10\({}^{6}\), and 1.0, respectively. The standard deviations on \(\ln(\mathrm{V}_{h})\) and E are 0.1. The standard deviations on \(\sigma_{\mathrm{x}}\), \(\sigma_{\mathrm{y}}\), \(\sigma_{\mathrm{z}}\), and \(\sigma_{\mathrm{t_{0}}}\) are 0.2. The correlation length \(\lambda\) is 5. Using the goafs' area and location based on the statistical analysis of historical data as the prior models, the P-wave arrival time data collected from monitoring were used to invert the velocity field for each plane, following the method described in Section 2. After obtaining the wave velocity imaging results for each section plane, goaf areas are rapidly identified based on the differences in wave velocity between non-empty and empty anomalous areas. Subsequently, smooth curves connect grid units situated at the periphery of these mined-out zones, delineating their boundaries. Figure 10 presents the layout of the results for different sections based on the physical detection results. In these figures, the red area denotes non-extracted rock mass, while the white grid area indicates the goaf regions; the blue line delineates the boundaries of these goaf areas. Table 2 presents the goaf area of different slopes derived from the statistical analysis of historical data and physical detection, along with a comparative analysis of these two results. Owing to spatial constraints, Table 2 presents only a subset of the data. A comprehensive dataset can be found in the Excel file titled 'The Goaf Area of Various Stopes Derived from Statistical Analysis of Historical Data and Physical Detection.' 1. Figure 10a is a planar representation of the physical detection results for the first section of the tantalum-niobium mine. Analysis of Figure 10a reveals five distinct goaf zones within this section. Historical records corroborate this observation, confirming that there are indeed five identified slopes in the first section, specifically numbered 68160, 68132, 68134, 68136, and 68138. 2. Figure 10b represents the physical detection results for the second section of the tantalum-niobium mine. Statistical analysis reveals the presence of three mineral \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sensor Serial Number**} & \multicolumn{3}{c}{**China Geodetic Coordinate System 2000**} \\ \cline{2-4}  & **X** & **Y** & **Z** \\ \hline
1 & 2,993,105.819 & 38,477,928.200 & 656.803 \\
2 & 2,993,170.253 & 2,993,170.253 & 648.763 \\
3 & 2,992,894.087 & 2,992,894.087 & 655.071 \\
4 & 2,992,595.972 & 2,992,595.972 & 653.044 \\
5 & 2,993,121.047 & 2,993,121.047 & 716.031 \\
6 & 2,993,079.673 & 2,993,079.673 & 755.657 \\... &... &... &... \\
23 & 2,992,987.938 & 2,992,987.938 & 894.922 \\
24 & 2,993,043.968 & 2,993,043.968 & 881.048 \\
25 & 2,993,097.857 & 2,993,097.857 & 894.841 \\
26 & 2,993,363.524 & 2,993,363.524 & 881.492 \\
27 & 2,993,611.819 & 2,993,611.819 & 898.803 \\ \hline \end{tabular}
\end{table} 
Table 1: Coordinates of sensor placement.