In order to clarify the seismic sedimentology interpretation method under coal seam interference, time-frequency analysis technology was used to analyze the seismic reflection frequency of the coal seam. Combined with seismic filtering, the seismic interference of the coal seam is suppressed through the sedimentological interpretation of stratigraphic slices with different dominant frequencies. The spatial distribution characteristics of the sedimentary system are determined by comprehensive mixed display technology. The results show that the delta plain subfacies are developed, which are composed of four distributary channels, crevasse splays, and swamps in the near north-south direction, and a large-scale channel migration zone is formed in the southwest.

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In order to accurately predict reservoirs with similar physical properties between sandstone and surrounding rocks, this study takes Carboniferous-Permian system in southeastern Ordos Basin as an example, and puts forward a multi-attribute probabilistic neural network prediction method based on acoustic time difference compaction correction. Firstly, the time-frequency analysis method is used to divide the frequency of the original acoustic time difference curve, and the low-frequency long-trend correction of all wells is carried out based on the low-frequency components of standard wells. Then, it is frequency-divided and fused with the original high-frequency components to form new acoustic time difference data, and the long-term trend decompaction correction of acoustic time difference is completed, so that the overlapping area of acoustic time difference between reservoir and surrounding rock is reduced. Multi-attribute probabilistic neural network is an algorithm based on sampling points, before reservoir prediction, inversion parameters must be optimized to save calculation time and reduce prediction error. There is a high correlation between longitudinal velocity and lithologic change trend on the inversion profile of sound velocity. The comparison results of various inversions show that the velocity profile of multi-attribute probabilistic neural network has the highest coincidence rate and high resolution with the velocity curve of logging acoustic wave. The example shows that this method has high accuracy in predicting reservoir thickness, especially for the strata with little difference in physical properties between reservoir and surrounding rock.