Curve and Surface Fitting of Coal Seam Based on Spatial Correlation and Genetic Algorithm

In order to more accurately show the changes in the spatial occurrence of coal seams in the fully mechanized coal mining face, and enhance the expressiveness and utilization of geological data. Based on the traditional 7th-order CatmullRom spline function, combined with the spatial correlation among the sampling points of coal seam, and with the help of genetic algorithm to solve the optimization of spline curve shape factor, the CatmullRom spline fitting algorithm(SCGA-CatmullRom) based on spatial correlation and genetic algorithm optimization was proposed. The algorithm was used to fit the surface of some coal seams in 16402 fully mechanized coal face. It is showed that the curve and surface model built can truly reflect the space occurrence status and distribution direction of the coal seam in the fully mechanized mining face, and provides a reliable basis for the comparison decision of production schemes.