Most methods using transmitted channel wave (TCW) prospecting to quantitatively detect the thickness of coal seams based on the statistic relationship of group velocity in certain wave bands to the thickness of coal seams cannot be applied universally. To establish a universal applicable method, we first obtained the theoretical dispersion curve of TCW using the generalized reflection–transmission coefficient method and the 1-D horizontal multilayer velocity model, performed iteratively match calculation using the inversion model and the genetic algorithm and analyzed the distributive characteristics of shear wave velocity of coal and rock formations at a certain depth. We then obtained the 3-D velocity images of the coal seam working face based on TCW data using the 3-D back-projection technology. According to the changes of shear wave velocity at the coal–rock interface and the rate of inversion velocity change, we further proposed the quantitative discriminant model for coalbed thickness. Based on the model, we quantitatively interpreted the thickness of the coal seam by computing the depths corresponding to the extremes of the positive and negative rate of the shear wave velocity change and obtained the distribution characteristics of the coal thickness in the working surface. To verify the feasibility and validity of the proposed model for coalbed thickness, we conducted a 3-D physical similarity model experiment and subjected the collected two-component TCW data to inversion calculation and compared the obtained coal seam thickness with the known model parameters. Overall, our study achieved the universal 3-D quantitative detection of coalbed thickness and provided technical supports for intelligentized coalbed mining.