# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 1 Introduction  
China is a major energy producer and consumer, mainly relying on coal. Its coal mines employ intelligent unmanned operational approaches, towards a safer and more efficient production environment [1]. Although coal resources are abundant in China, the mining conditions are complex, especially with the increase of mining depth [2]. Hence, precise prediction of coal thickness and of hidden geological anomalies in the working face is fundamental to realization of intelligent and unmanned mining. This is also directly related to the transformation of the whole process, and to the safety of underground workers [3]. The precise identification of coal thickness has long been a subject of research for coal enterprises and for related scholars. Various drilling and geophysical methods have been used [4], but each has its own characteristics and application limitations. The drilling method (as the most direct method for identifying coal thickness) is limited by cost, offering a limited amount of data [5]. Thus, there are blind zones, causing potential safety threats. The radio wave tomography technique has the advantagesof low cost and high efficiency. However, the coal thickness can only be judged qualitatively rather than quantitatively, and the detection of coal thickness is poor, with large seam fluctuations [6]. Three-dimensional seismic exploration is a method of detecting coal thickness on a large scale, but the detection accuracy is low, due to the large detection distance and the fact that it is easily affected by overlying strata. Moreover, the accuracy is further affected -by the increase of the future mining depth [7; 8]. As a guided wave propagating within the coal seam, the channel wave was first activated and recorded by [PERSON] in 1955 [9]. Due to its long propagation distance, its strong energy, the easy identification of waveform, and the high-resolution imaging effect, the channel wave is widely used in the tomographic inversion of coal thickness [10; 11; 12; 13; 14]. There are many tomographic inversion algorithms based on the channel wave's travel time, including Back Projection Technique (BPT), Algebraic Reconstruction Technique (ART), Simultaneous Iterative Reconstructive Technique (SIRT), and Least Square QR-factorization (LSQR) (where Q is an orthogonal matrix and R is a triangular one) [15; 16; 17]. Among them, the SIRT algorithm is the most widely used in seismic tomography inversion, because it is more suitable for sparse, irregular, and low signal-to-noise ratio (SNR) measured data, and thus it is easy to add prior information [18; 19; 20]. Most of the above traditional inversion methods mainly rely on the linearized form of the problem. An appropriate initial model is used to minimize the objective function by repeatedly moving to an improving neighboring set of solutions until the allowable conditions are met [21]. The result of the tomographic inversion reconstruction strongly depends on the initial model. However, as the coal seam distribution medium is heterogeneous, channel wave tomography is a complex, nonlinear inversion process involving a large number of unknown parameters. Therefore, due to the fact that in complex media the objective function has many local minima, it is prone to local optimal solutions if the initial model is not selected properly. This can lead to an adverse impact on the accuracy and stability of coal thickness inversion results. In many cases, searching for a global minimum of the objective function is a key technology in coal thickness inversion. Genetic Algorithms (GA) constitute a stochastic global search and optimization method which imitates the evolution mechanism of organisms in nature. GAs have the characteristics of global optimization, strong adaptability, and strong robustness [22]. They have been widely used in function optimization, nonlinear parameter estimation, pattern recognition, image processing, and other fields [23; 24; 25; 26; 27] since they were proposed by [PERSON] in 1975 [28].