## 3 Signal Processing While Drilling
### Feature Selection  
To predict the strength of a coal rock mass, it is essential to first evaluate all parameters in the original data and select those that are appropriate as input features. During the contact drilling process between the drill bit and the rock mass, variations in the sensor parameters can most effectively reflect changes in the state of the rock mass.  
The Pearson correlation coefficient is a statistical measure used to assess the degree of linear correlation between two variables. It is widely employed in data analysis across the social sciences, natural sciences, and various other fields. This coefficient is calculated based on the covariance and standard deviations of the two variables, and the significance of the correlation is evaluated through hypothesis testing. The range of the Pearson correlation coefficient is from \(-1\) to \(1\); the greater the absolute value, the stronger the correlation. A two-tailed significance test is utilized to determine whether the statistics of a sample significantly differ from a specified parameter value. If the p-value of the statistic is less than the significance level, it is concluded that the sample statistics are significantly different from the parameter value. Many researchers have utilized the Pearson correlation coefficient and significance tests to investigate the relationships between variables. Consequently, SPSS (R26) statistical software was employed to analyze the correlation between LWD signals and uniaxial compressive strength. By applying the Pearson correlation coefficient and the two-tailed significance test, a correlation analysis table was generated, illustrating the relationship between uniaxial compressive strength and LWD signals, as presented in Table 3. The results indicated that the Sig between each variable was less than 0.05,  
Figure 4: Multi-level wavelet coefficient decomposition diagram.  
Figure 5: Comparison diagram of the actual vibration signal before and after noise reduction.  
suggesting varying degrees of correlation among different drilling signals and between drilling signals and uniaxial compressive strength. The Pearson correlation coefficient ranges from \(-1\) to 1, with larger absolute values signifying a stronger correlation. The Pearson correlation coefficients between drilling signals and uniaxial compressive strength are as follows: torque (0.610), pressure (0.429), vibration (0.326), rotational speed (0.314), speed (0.124), and inclination angle (0.088). There is a strong correlation between torque and uniaxial compressive strength, while pressure, vibration, and rotational speed exhibit moderate correlations with uniaxial compressive strength. In contrast, speed and inclination angle show weak correlations with uniaxial compressive strength. Therefore, torque (T), pressure (F), vibration (A), and speed (N) are considered as input characteristics.  
During the drilling process, the data obtained from the sensors primarily focus on the average value and fluctuation amplitude. Consequently, the mean value (Fm, Tm, Am, Nm), standard deviation (Fn, Tn, An, Nm), and peak-to-peak value (Fs, Ts, As, Ns) of the drilling section data processed by the drilling rig are selected as the model's input parameters. The final input parameters consist of the aforementioned 12 eigenvalues.  
Using data processing methods, four drilling state parameters (pressure F, torque T, vibration A, and rotational speed N) and one geomechanical property of the rock mass (uniaxial compressive strength) of the drilling rig were processed to create a rock-machine interaction database. This database includes mean, standard deviation, and peak-to-peak values. A total of 460 datasets were collected, providing a solid foundation for the subsequent learning and validation of algorithmic models.