Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm

Precise prediction of coal thickness is of the utmost importance in realizing intelligent and unmanned mining. As the channel wave is characterized by an easily recognizable waveform, a long propagation distance, and strong energy, it is widely used for coal thickness inversion. However, most traditional inversion methods are local in nature, and the inversion result is probably not optimal in the global scope. This paper introduces the GA-SIRT hybrid approach, which combines Genetic Algorithms (GA) and Simultaneous Iterative Reconstructive Techniques (SIRT) in order to deal with the above problem and to improve the accuracy of coal thickness inversion. The proposed model takes full advantage of the strong global search capability of GA and of the fast local convergence rate of the SIRT. Moreover, it inhibits the poor local search ability and the local optimal value effect of the GA and the SIRT respectively. The application of the GA-SIRT in the Guoerzhuang coal mine has significantly enhanced its accuracy, stability, and overall computational efficiency. Hence, the introduced novel hybrid model can precisely resolve and identify the coal thickness according to the channel wave. It can also be extended to other geophysical tomographic inversion problems towards the reduction of potential local optimal solutions.