Deep coalbed methane exploration in the Ordos Basin holds significant potential, yet the frequent development of coal gangue severely hinders productivity and complicates horizontal well deployment. Accurately characterizing the spatial distribution of these thin coal gangue layers remains a critical challenge, particularly when constrained by limited seismic resolution and sparse well control. To address this challenge, we propose a novel paleogeomorphology-constrained stochastic seismic inversion workflow applied to the Benxi Formation in the YL area. This approach integrates petrophysical analysis with paleogeomorphic restoration, identifying natural gamma as the most sensitive lithological indicator. Under hierarchical geomorphic constraints, geostatistical simulation was utilized to predict the three-dimensional spatial probability of coal gangue occurrence. Quantitative validation demonstrates a robust correlation between coal gangue thickness and natural gamma response, with the inversion results achieving an accuracy of 81.82% in blind well validation. Spatially, gangue development is controlled by paleotopography, with higher probabilities concentrated in paleo-highs and slopes associated with stronger hydrodynamic conditions, while paleo-depressions exhibit superior coal continuity. This study not only overcomes the resolution limitations of traditional inversion in sparse-well areas but also provides a rigorous quantitative geological basis for sweet-spot identification and trajectory optimization in deep coalbed methane development.