The well log inversion has been widely applied to forecast coal texture distribution due to its effectiveness and low investment. However, most research missed the low resolution of well logging data caused by the “boundary effect”, which make it difficult to identify thin layers. Here a method to improve the vertical resolution of logging curves by Wavelet Transform was applied. The original logging curve was decomposed by two-level wavelet decomposition using sym 8 mother wavelet, and then the high-frequency wavelet coefficient (d₂) was weighted to reconstruct a new log curve to enhance the resolution of the thin-layer. Based on 160 reconstructed training logging datasets, the Linear Discrimination Analysis was used to establish a quantitative coal texture identification model by reducing the dimensionality of the data points and projecting them into a two-dimensional space. The validation results of coal texture are in good agreement with the coal core. Compared with the non-reconstructed curve, the recognition accuracy is greatly improved, especially for layers with a thickness < 20 cm. The combination of Wavelet Transform and Linear Discrimination Analysis provides a new way to predict coal texture using geophysical logging data. This method has been successfully applied to multiple (>20 layers) and thin (generally <2 m in thickness) seams in the western Guizhou province of China, with recognition accuracy >85%.

由于测井反演效果好，投资少，已被广泛应用于预测煤的质地分布。但是，大多数研究都忽略了由“边界效应”引起的测井数据分辨率低的问题，这使得很难识别薄层。在此应用了一种通过小波变换提高测井曲线垂直分辨率的方法。原始测井曲线通过使用sym 8母小波通过两级小波分解而分解，然后通过高频小波系数（d ₂加权）以重建新的对数曲线，以增强薄层的分辨率。基于160个重建的训练测井数据集，通过减少数据点的维数并将它们投影到二维空间中，使用线性判别分析建立了定量的煤质地识别模型。煤质地的验证结果与煤芯吻合良好。与未重建的曲线相比，尤其是对于厚度<20 cm的图层，识别精度得到了极大的提高。小波变换和线性判别分析的结合提供了一种使用地球物理测井数据预测煤质地的新方法。此方法已成功应用于多层（> 20层）和薄（通常<