# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 4 Results and Discussion
### Discussion  
In this study, the BP neural network served as the model for the coal thickness outcome prediction (Figure 12), which aligned closely with the actual results. The coal thickness at drill hole locations is represented in red. From the thickness variation trend map of the No. 2 coal seam, it can be observed that the thickness of the No. 2 coal seam ranges from 0.12 m to 6.84 m. The thinnest section is located in the northwest corner within the erosion zone, while the thickest section is found near the boundary in the southwest corner. The thin layers are distributed in an irregular banded pattern. Based on the practical application of impedance inversion methods, the results demonstrate that the BP neural network inversion model based on seismic multi-attributes achieves relatively high accuracy with small errors, As shown in Figure 13, where the x-axis represents revealed drill holes and the y-axis represents relative errors, the comparison reveals the following observations: the impedance inversion shows relative errors ranging from 0.03% to 6.59%; LB12 is the well location constrained by logging inversion; and LB12 had the smallest relative error. The BP neural network model exhibited relative errors ranging from 0.11% to 1.36%. The BP neural network demonstrated optimal performance with the smallest fluctuation range and maximum relative error performance. The residual analysis of the BP neural network prediction shows a minimum value of \(-0.05425\), a maximum value of 0.03865, and a standard deviation of 0.02714. In comparison, the residual analysis of the well log impedance inversion had a minimum value of \(-1.0334\), a maximum value of 0.17914, and a standard deviation of 0.08415. Figure 12: Coal thickness prediction results. To better assess the stability of coal thickness predictions, this study analyzed the error comparisons under different attribute combinations using the BP neural network model as follows:  
* Replacing one of the top four optimal attributes with the fifth-ranked attribute, high cross-section, as shown in Figure 14;
* Replacing two of the top four optimal attributes with the fifth and sixth-ranked attributes, high cross-section and maximum energy level, as shown in Figure 15.  
In Figures 14 and 15, the x-axis labels \"B\" represent the original system's optimized attribute combinations, while other letters denote alternative combinations. \"IQR\" represents the interquartile range, a commonly used statistical measure to assess data variability. The IQR is the difference between the third quartile (upper quartile) and the first quartile (lower quartile). A range of 1.5 times the IQR is often used to identify outliers. Comparing the relative errors in Figures 13 and 14 revealed the following:  
Replacing one attribute led to a significant increase in the maximum relative error in coal thickness prediction, from <10% to <18%; replacing two attributes resulted in an even larger increase in the maximum relative error, exceeding 20%; this increase indicates increased data variability and the potential for outlier errors. Thus, the optimized combination of attributes significantly influences the final prediction error. It determines the magnitude of prediction errors effectively. The premise of accurate coal seam thickness prediction is the availability of high-quality data imaging. For this region, the key challenge lies in addressing the significant topographic variation of nearly 300 m. In this paper, we accurately determined the refraction static correction values for the Green Mountain first-break refraction static correction method, successfully separating the long-wavelength and short-wavelength components. By effectively resolving the severe jumps between neighboring channels using the short-wavelength component, we then calculated the residual statics correction values, as shown in Figures 16 and 17. This approach significantly enhanced the quality of seismic data and laid a solid foundation for the application of two distinct methods.