Coal seam15 is one of the main minable seams in Mine No.5 of Yangquan Coal Industry(Group) Co., Ltd., There is K2 limestone developed in the roof of the seam. The limestone is not a good seismic reflection inter-face. It is difficult to trace continuously the limestone interface by conventional seismic profiles. The logging curves of K2 limestone show high density and high apparent resistivity. We used integration of density and apparent resistivity logging curves to generate pseudo density curves, then to get data of formation lithology based on model inversion. So we can identify the geometrical shape and the thickness of the limestone. We selected 9 seismic at-tributes to constitute the training samples for neural network so as to conduct inversion of the neural network for the porosity and the apparent resistivity and finally to predict the aquosity of the limestone.%阳煤五矿主要可采煤层15号煤层顶板发育的 K2灰岩不是良好的地震波反射界面，常规地震剖面很难连续追踪。测井曲线上的 K2灰岩表现为高密度和高视电阻率异常，采用密度与视电阻率两种测井曲线融合生成拟密度曲线，基于模型反演得到地层岩性数据体，从而识别灰岩的赋存形态与厚度；采用概率神经网络反演的方法，优选出9种地震属性，构成神经网络训练样本，对灰岩的孔隙度和视电阻率进行神经网络反演，预测灰岩的富水性。
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