# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 1 Introduction  
Coal resource is a valuable non-renewable energy source. The average coal mining rate in China's large-scale mining areas is about 30% to 40% and the recovery rate for small coal mines is even less than 10% [1]. The low mining rate leads to large waste of resources. At present, the mechanized mining of coal mines is seriously inadequate in China. According to statistics [2], China's mechanization in key coal mining areas is 75% and the national average mechanization level is less than 40%. Therefore, the development of automatically and intelligently coal mining techniques is an urgent issue to be solved in China. Due to the rigors of the mining environment and the coal mining safety, the automation technologies are paying great attention at present. The indirect prediction of the entire coal-rock interface is critical in achieving automation and intelligence of coal mining through borehole data and partial mining data. Coal seam thickness is important information in coal mine design and mining. The effective detection of coal-rock interface and accurate prediction of coal seam thickness not only provides the good geology of coalmine, but it is also crucial for high-yield and high-efficiency mining and the automatic height adjustment of the shearer drum at a man-less working face [3]. The cutting data obtained in pre-mining is relatively accurate in the numerous mine geological data, but due to thelimited amount of cutting data, an appropriate interpolation method should be adopted in order to predict the complete set of the coal seam thickness data. Among the large amount of interpolation methods, Kriging interpolation is widely used in the field of coal mining. It takes the spatial correlation into account when dealing with data, achieving good performance in most cases [4]. In the Kriging interpolation, the types of variation function model are finite, which make the variation function very difficult in describing the spatial distribution characteristics of true data. In order to overcome this shortage, an improved interpolation, called Support Vector Machine-Kriging interpolation (SVM-Kriging), was proposed in [5]. The SVM-Kriging uses the Least Square Support Vector Machine (LS-SVM) to fit the variation function and to directly get the optimal variation function for the real interpolated field by using SVM to automatically fit the variation function curve. In [6], the Kriging interpolation method was used to study the coal quality prediction model of cutting coal, and the parameters of the optimal variation function model and experimental variation function are determined based on data characteristics. The spatial distribution of ash and heat is predicted by using the Kriging interpolation method. In [7; 8], a method that is based on dimensionless parameters and SVM was proposed for coal-rock interface identification. In the existing methods, the spatial structure and spatial distribution in the region of the coal seam geology transition are seldom taken into account. Moreover, the coal seam thickness changing is considered only for the marching direction, and the changes in other directions are usually ignored. As a result, the accuracy of coal seam thickness prediction is affected by such simplifications. In recent years, the Gaussian process (GP) regression has been demonstrated to handle the uncertainty in predictive analytics problems with superior predictive performance. GPs can model complex systems, whilst handling uncertainty in a principled manner. GPs can exploit prior information in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data. Ref [9; 10] used the Gaussian process regression algorithm for time series prediction and battery life prediction, and various advantages over other data-drive and mechanistic methods were demonstrated.