Multi-Fractal Model of Coal Resources Distribution and Its Application in Spatial Distribution Law of Coal thickness

Liu Xing, Hu Baolin, Jiang Songmei
2017 International Journal of Signal Processing, Image Processing and Pattern Recognition  
Most study on the coal thickness distribution mainly focus on geology analysis by geophysical data currently, some researchers apply trend surface method to extract hidden information from those geological data to reveal the law of coal thickness distribution effectively. This paper proves that the coal resources and coal thickness distribution follows fractal and multi-fractal model based on the principle of coal depositing, and demonstrates the fractal characters of global coal resources and
more » ... hina coal resources by the statistical data. Further more, this study utilizes 168 coal thickness of M10 from Huaibei coal field through dills to separate the background and abnormity map by S-A method with some necessary pre-processing, the resulted background map can be looked as a trend map of trend surface analysis and the abnormity map is as residual map according to the mathematical model, the contour map of background shows the deposition rule that the thick coal distributes around the underlying sand with shallow water and the thin coals are on the thick sand and split bay with stronger spatial corresponding relationship, while the abnormity map shows the secondary coal thickness change is related to the process of filling and level up, the location is also coincident with the underlying deposit environment. The result by S-A method reveals the coal thickness distribution more clearly and precisely and more close to the deposition rule contrasted with the result by trend surface analysis, it is concluded that the coal thickness distribution follows the multi-fractal model and the S-A method is effective for those geological data processing. .So the processing and analysis of geological data is favor of multi-fractal model for the self-similarity result from the nonlinear dynamic geology process.
doi:10.14257/ijsip.2017.10.4.08 fatcat:6f36zalofre73a7fa4vdl3idba