Application of fault information dimension in forecast of relative complexity degree of mine structures

作者：Xia Yu Cheng; Bai Hong Mei; Sun Xue Yang

来源：Journal of Hunan University of Science and Technology, 2005, 20(2): 1-4.

In order to help coalmine search better regions of mining geological conditions in the mining field, an area studied was divided into cells, and the data, including fault information dimensions and its affection factors, of each cell in worked sections were separately counted. The results of gray relationship analysis and progressive regression analysis of the data showed that the fault information dimension was a synthetical reflection of fault characteristics such as number, density, length, intensity, and there was a positive correlation between fault information dimension and relative complexity degree of mine structures. An artificial nerve network was trained with the pattern of fault information dimensions and its affection factors except fault indexes from every cell in worked sections, and the network meet precision expectations was used to forecast fault information dimension of different cells in mining field. It is validated that the veracity is about 90% by the contrast between statistics and forecast data of the fault information dimension from every cell in worked sections of Dongpang Coal Mine.

• 出版日期2005