An ANN-Based Inversion Method for High-Precision Layer Thicknesses in Coal–Rock Recognition

During the identification of shallow coal-rock layers using ground penetrating radar (GPR), inversion of the air and coal thicknesses by GPR response is the key to achieving intelligent control over the orientation of the Shearer drum and GPR. The relationship between the offset GPR response and the real thicknesses of complicated shallow layers is uncertain and there is random noise present in the environment. A novel method based on artificial neural network (ANN) is proposed to invert the real thicknesses by the apparent thicknesses picked up from the GPR response. First, a relationship model between the real thicknesses and the air-coupled GPR apparent thicknesses of the complex double layers is established through qualitative analysis. Then two ANN architecture schemes are proposed for inverting the real thicknesses of air and coal, respectively. Afterwards, the ANN models are trained by simulation data and are then improved with regard to network input, network structure, and dataset size. The robustness of this approach is then examined under various trace interval and signal-to-noise ratio (SNR) scenarios. Finally, the proposed method is applied to physical model experiments and contrasted with traditional methods. The results show that the proposed method significantly improves the accuracy of the air and coal thicknesses measured by air-coupled GPR. The maximum and average errors of the air thicknesses are 4.97 mm and 1.15 mm, respectively, with those of the coal thicknesses of 6.04 mm and 1.60 mm. This method can achieve high-precision measurement of shallow layer thicknesses, supporting coal mining automation.