# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions  
[PERSON]  
1 MNR Key Laboratory of Saline Lake Resources and Environments, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China; [EMAIL_ADDRESS] (T.D. ); [EMAIL_ADDRESS] (Z.N.) [PERSON]  
2 School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China 2  
between thin layer thickness and its reflection amplitude in an infinitely homogeneous medium, surpassing purely geometrical methods to determine the limits of thin layer thickness [7]. Subsequently, [PERSON] and [PERSON] defined thin layers using the quasi-linear relationship between composite reflection wave amplitude and thickness [8]. For different reflection coefficients, the sinusoidal response amplitude of wedge bodies is approximately linear with d/\(\lambda\). For a given reflection coefficient, thin layers are defined as those where the error between the true amplitude curve and the linear approximation is less than 10%. Currently, coal thickness determination in production typically uses borehole interpolation methods; however, due to the limited density and number of boreholes [9], the results have significant errors and cannot effectively guide operations. Although underground channel wave exploration has high accuracy for coal thickness, its application is limited by time and data volume; therefore, it plays a corrective role in intelligent mining processes [10; 11]. To solve the problem of the quantitative interpretation of coal thickness, many scholars have explored and studied seismic attributes [12; 13]. Seismic attribute methods can effectively reflect the characteristics of thin layers in seismic information, establishing a physical relationship between thin layers and seismic attributes, providing a new approach for quantitative determination of coal thickness. Some researchers believe that coal seam thickness is related to the ratio between the integral of the amplitude spectrum of coal seam reflection waves and the first moment of the seismic wavelet amplitude spectrum [14]. To demonstrate the correlation between amplitude attributes and coal thickness, a wedge-shaped coal seam model was established for verification [15]. However, single seismic attribute methods use only one type of seismic attribute parameter, leading to significant random errors and limited calculation accuracy [16]. Neural network algorithms and multiple regression models can provide effective predictions by simulating the input feature set (such as seismic waveforms) and the expected output values (such as phase types, arrival times, etc.) [17; 18]. For coal thickness determination, training and processing the multi-attribute features of incomplete seismic data samples with unique generalization capabilities can reduce interpretational ambiguity and improve interpretation efficiency and accuracy. Analysis shows a close relationship between coal seam thickness and seismic amplitude and frequency attributes. Training and processing the multi-attribute features of incomplete seismic data can enhance interpretation accuracy and efficiency, yielding good results in the inversion of seismic wave attribute parameters to coal thickness [19; 20]. On the other hand, the method of predicting coal thickness using well log-constrained impedance inversion improves prediction accuracy [21]. High-resolution stratigraphic impedance data can provide a good geological description of thin layers. By fully acquiring high-frequency information and retaining low-frequency components, the bandwidth limitations of seismic data can be compensated for. The low- and high-frequency information is obtained from well log data, while the structural features and mid-frequency range depend on seismic data. Seismic inversion inherently carries a degree of uncertainty. However, by conducting comparative studies of different methods using the same seismic exploration data, this uncertainty can be significantly reduced.