## 4 Engineering Case Application
### Dataset Creation  
The initial MWD data contains 11 parameters, of which 10 are drilling parameters and the other is rock class. The correlation between these parameters were analysed by Kendall coefficient and the key drilling parameters were selected by the Tau\({}_{\text{b}}\) value; the results are shown in Table 3, where the \(p\)-values are less than 0.05, i.e., the results are reliable. Table 3 shows that the parameters with higher positive correlations with the surrounding rock classes are EV and ELT, and those with higher negative correlations with the surrounding rock classes are drilling speed and torque. This indicates that when the rock integrity is good, the penetration rate of drilling is low and the energy required to break the rock mass is high, which is in line with the actual situation. As the absolute Tau\({}_{\text{b}}\) values of the rotation speed, water supply rate, and water supply pressure were less than 0.1, these three parameters were excluded. The Tau\({}_{\text{b}}\) values of the penetration energy and beat per minute were 1.0, while the Tau\({}_{\text{b}}\) values of EV and ELT were 0.9, with high positive correlations among the parameters. To avoid parameters with high correlations interfering with each other during training, beats per minute and ELT were also excluded. The selected key parameters included the penetration rate of drilling, torque, thrust, penetration energy, and EV energy. The subsequent optimal deep neural network model with optimal hyperparameters was utilized to develop the 10-fold cross-validation mean accuracy with different numbers of key drilling parameters (Figure 12). The highest accuracy was achieved for the five key drilling parameters, which were selected by Kendall coefficient. The result illustrates the rationality of the key parameters selection method. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Penetration** \\ **Rate of** **Dilling** \\ \end{tabular}  & \multirow{2}{*}{**Torque**} & \multirow{2}{*}{ \begin{tabular}{c} **Rotation** \\ **Speed** \\ \end{tabular}  } & \multirow{2}{*}{**Thrust**} & \multirow{2}{*}{ \begin{tabular}{c} **Penetration** \\ **Energy** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Beat** \\ **Per** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Water** \\ **Surply** \\ **Rate** \\ \end{tabular}  } & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**EV**} & \multirow{2}{*}{**ELT**} & \multirow{2}{*}{ \begin{tabular}{c} **Rock** \\ **Class** \\ \end{tabular}  } \\ \cline{1-1}  
The abnormal values of the 27,607 sets of data were removed. Finally, data were min-max normalized. Because of the need for storing rock classes as variables, a one-hot encoding form was used to encode N states with N-bit status registers, representing classes III, IV, and V as (0, 0, 1), (0, 1, 0), and (1, 0, 0), respectively. The MWD dataset was packaged as a sample of 20 depth-sequences to obtain a total of 1300 samples and 26,000 sets of data, with a significant imbalance in the percentage of each class of surrounding rocks, as shown in Figure 13a. The data of the first nine advanced drilling holes were divided into the training set, and the data of the last hole was divided into the test set. The training set contained 1200 samples and 24,000 sets of data; the proportion of each rock class are shown in Figure 13b (from III to V are 60.25% (723 samples, 11,460 sets), 23.92% (287 samples, 5740 sets), and 15.83% (190 samples, 3800 sets), respectively). In the training set, 10-fold cross-validation was performed in a ratio of 9:1. As the dataset was unbalanced, the stratified 10-fold method was used to pass the information on the rock-class labels when dividing the dataset to ensure that the proportions of samples of different rock classes in the validation set were the same as those of the original samples.