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The small structures can directly affect the admissibility of the coal seam, the mining schedule, cost and safety. Seismic attributes extracted from seismic data can be used to identify small structures; However, geophysical methods often have multiple solutions, this will make the results of interpretation become unreliable. In this paper we will use image fusion technology to fuse a variety of seismic attributes, and then make full use of the complementarity of different attributes to reduce the multiple solutions, and make the seismic attributes more accurately in predicting small structures in coalmine.

The conventional inversion can only get P-impedance data, but neural network inversion can obtain various kinds of well-log attributes data by getting the nonlinear relationship through training the data of seismic attributes and well-log attributes, and then apply the nonlinear relationship to the whole seismic data volume. In this paper, we use multilayer feed forward neural network, probability neural network and radial basis function neural network to carry out the log-density inversion research, and obtain three pseudo density data volumes. We compare the effect of the inversion results and analysis the density distribution in spatial domain through section and slice data, at last we predict the stability of the coal seam’s floor in more reasonable way.