# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 4 Experimental Results and Analysis
### Coal Seam Forecasting Based on Real Data  
In this experiment, the real data of a thin coal seam face from a coal mine in northern Shaanxi Province of China is used. The real data is interpolated by the proposed RBF-Kriging method for satisfying the structural element integrity. Figure 11 shows the interpolated surface. The sample point interval in working surfaces is 1.5 m and the depth-web is 0.8 m. The total number of sample points for each cutting is 10. For the coal seam data, the training sample pairs are obtained while using the data preprocessing method that is described in Section 3.1, in which each pair of sample contains one structural element with eight points and one output corresponds to it.  
Figure 10: Three-dimensional (3D) visualization of the coal seam surface obtained from the simulated experiment: (**a**) real surface and (**b**) prediction surface.  
Figure 9: Test-Prediction Data Graph of improved algorithm.  
30 pairs of real data are selected in this experiment, of which 20 data pairs are used as the training data and 10 data pairs are used as test data. Figure 12 presents the prediction results using the proposed STTP-GPR algorithm.  
The absolute average error in the test set remains around 0.025. The prediction error at the third point and the last two points of the test set is relatively high, due to the coal seams being mutational at these points. Analysis of the prediction error on all test sets shows that the overall prediction error is less than 0.031. From Figure 12, it can be seen that the prediction value of STTP-GPR algorithm at second data and the last three data in the test set has larger error, but their error is less than 0.03 by taking into account the spatial structure information and the maximum error in test set is less than the error that is predicted by GPR algorithm, as depicted in Section 4.3. From the real coal seam data, it can be seen that the frequency of local fluctuations in the coal seam during its evolution is relatively large; in the proposed STTP-GPR algorithm, the transition probability is mainly divided by human experience in the calculation process, resulting in the inaccurate division of the interval or a failure to describe the structure information. In the future, we will focus on the study of how to accurately express the local spatial structure information of the coal seam and the relationship between the local spatial structure information and the global spatial structure information.  
Figure 13 shows the real surface and predicted surface in order to visually represent the pros and cons of the prediction algorithm in the coal-rock interface identification process while using the real data. The predicted surface shown in Figure 13 is the upper surface of the coal seam is obtained from the predicting and known data. The real surface is obtained from the known data in the test set. It can be seen that the predicted surface can accurately express coal seam movements and changes in coal seam thickness, especially in the local district.  
Figure 11: Interpolated surface from the real coal seam data  
Figure 12: Prediction results obtained by using the proposed STTP-GPR algorithm and real coal seam data.