煤层精准定位是无人采煤的关键技术，煤层厚度预测是煤田地震资料解释的重要研究内容之一。参考实际地层厚度及物性参数，构建含楔形煤层的正演模型，通过地震剖面正演和地震属性提取、优化，对比分析信噪比和多种回归方法对煤层厚度预测的影响。研究结果表明：部分地震属性与煤厚相关性较强，可以用于煤厚预测；地震属性间的信息冗余不可忽略，但基于主成分分析和多维标度的地震属性优化结果无本质区别；当信噪比较低(10 dB)时，随机森林回归算法的均方根误差最小(1.07)，支持向量机回归算法的误差居中(1.15)，多元线性回归算法的误差最大(1.84)；当信噪比较高(25 dB)时，支持向量机回归算法的误差最小(0.05)，随机森林回归算法的误差居中(0.11)，多元线性回归算法的误差最大(0.20)；输入数据信噪比对煤厚预测有明显影响，信噪比越高、预测效果越好。基于地震属性优化及支持向量机回归的煤厚预测方法，是实现薄煤层厚度高精度解释的一种有效途径。

Accurate location of coal seams is the key technology of unattended mining and predicting the thickness of coal seams is an important content in the seismic interpretation of coalfields. This study constructed a forward model involving wedge-shaped coal seams by referencing the actual thickness and physical properties of strata. Moreover, this study compared and analyzed the effects of signal-to-noise ratio (SNR) and regression methods on the prediction of the coal seams’ thickness through the forward modeling of seismic profiles and the extraction and optimization of seismic attributes. The results of this study are as follows: (1) Some seismic attributes were strongly correlated with, and can be used to predict, the thickness of coal seams; (2) The information redundancy among seismic attributes cannot be ignored. However, there was no essential difference between the seismic attributes optimized using principal component analysis (PCA) and multi-dimensional scaling (MDS); (3) When the SNR was low (10 dB), the root-mean-square (RMS) error of the prediction results of different algorithms was in the order of random forest regression (RFR, 1.07)< support vector machine regression (SVR, 1.15)< multivariate linear regression (MLR, 1.84); (4) When the SNR was high (25 dB), the RMS error of the prediction results of these algorithms was in the order of SVR (0.05)<RFR (0.11)<MLR (0.20); (5) The SNR of the input data had significant impacts on the prediction of the coal seams’ thickness, and a higher SNR corresponded to better prediction performance. Coal thickness prediction method based on seismic attribute optimization and SVR is an effective way to realize high-precision interpretations of the coal seams’ thickness.