# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 3 Case Study
### Data Preprocessing
The instrument's design incorporates a novel spatial array observation layout, adding calculations for parameters such as forward wave velocity, and expanding output formats to include 2D and 3D spectral maps of reflected energy. This results in higher prediction resolution and more intuitive and accurate detection results. A comprehensive list of key parameters is provided in Table 3.  
Subsequent to processing the data in accordance with the workflow depicted in Figure 7, prediction result images (Figure 8) are obtained. These images are presented using 3D visualisation. In these visualisations, the XOY and ZOY slices represent horizontal and vertical geological cross sections of the surrounding rock mass in front of the tunnel face. The analysis of anomalous zones within these slices facilitates the approximate determination of size and distribution of unfavourable geological bodies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Unit** & **Range** \\ \hline A/D Conversion & bit & 24 \\ Channel & N/A & 8 \\ Sampling Speed & \(\mu\)s & 0.1–\(1\times 10^{6}\) \\ Record Length & K & 1–23 \\ Dynamic Scope & v & \(\pm 10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main parameters of the HSP217 advance geological prediction instrument. In this study, geophysical experts interpreted the inversion imaging results in order to classify the characteristics of the reflected energy. These categories were then represented using one-hot encoding (see Table 4). This process involves converting categorical variables into a format that is readily interpretable by machine learning algorithms. This method involves the representation of m categories through m binary bits, with each reflected energy category assigned a unique numerical label [38]. Additionally, due to the inherent subjectivity in interpreting inversion imaging results, a degree of random noise was introduced to mitigate the influence of human factors. This random noise was introduced based on two indicators: length and category. It was primarily concentrated near the start and end points of the reflected wave chainage range. Specifically, the maximum (\(\eta max\)) and minimum (\(\eta min\)) noise proportions were first determined. The noise proportion, \(\eta\), is defined as the ratio of noise data points to the total number of data points within a specified chainage range. Subsequently, the Monte Carlo simulation method was employed to randomly generate the lengths of misclassified segments. The Monte Carlo simulation method is a numerical computation method based on random sampling, approximating solutions to complex problems through extensive \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reflection Anomaly** & **Rank** & **Code** \\ \hline None & I & \([1,0,0,0,0,0]\) \\ Weak & II & \([0,1,0,0,0,0]\) \\ Obvious & III & \([0,0,1,0,0]\) \\ Relatively Strong & IV & \([0,0,0,1,0]\) \\ Strong & V & \([1,0,0,0,0,1]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: One-hot encoding of reflected energy categories. Figure 8: 3D slice map from advance geological prediction. Figure 7: Description based on the flowchart components. random trials. Specifically, a random number \(\mathbf{r}\) is generated, where \(\mathbf{r}\in[0,1]\). The length of the misclassified segment, \(L\), is then determined using Equation (23):  
\[L=L_{min}+r\times(L_{max}-L_{min}) \tag{25}\]  
where \(L_{min}\) and \(L_{max}\) denote the minimum and maximum misclassification lengths, respectively. Subsequently, the generated misclassification length \(L\) is randomly classified to the nearest neighbouring category. The distance of the current data point from all categories is then calculated as follows:  
\[d_{i}=\sqrt{(x-x_{i})^{2}+(y-y_{i})^{2}} \tag{26}\]  
where \(x\) and \(y\) represent the chainage and reflected energy intensity of the current data point, respectively, and \(x_{i}\) and \(y_{i}\) are the mileage and reflected energy intensity of \(i\), the \(i\)th category.