TAN Xipeng, SHI Longqing, QIU Mei, XU Dongjing, JI Xiaokai, WANG Juan. Forecast of small fault based on SVM in Zhaoguan mine[J]. COAL GEOLOGY & EXPLORATION, 2015, 43(5): 11-14. DOI: 10.3969/j.issn.1001-1986.2015.05.003
Citation:
TAN Xipeng, SHI Longqing, QIU Mei, XU Dongjing, JI Xiaokai, WANG Juan. Forecast of small fault based on SVM in Zhaoguan mine[J]. COAL GEOLOGY & EXPLORATION, 2015, 43(5): 11-14. DOI: 10.3969/j.issn.1001-1986.2015.05.003
TAN Xipeng, SHI Longqing, QIU Mei, XU Dongjing, JI Xiaokai, WANG Juan. Forecast of small fault based on SVM in Zhaoguan mine[J]. COAL GEOLOGY & EXPLORATION, 2015, 43(5): 11-14. DOI: 10.3969/j.issn.1001-1986.2015.05.003
Citation:
TAN Xipeng, SHI Longqing, QIU Mei, XU Dongjing, JI Xiaokai, WANG Juan. Forecast of small fault based on SVM in Zhaoguan mine[J]. COAL GEOLOGY & EXPLORATION, 2015, 43(5): 11-14. DOI: 10.3969/j.issn.1001-1986.2015.05.003
Shandong Provincial Key Laboratory of Depositional Mineralization&Sedimentary Minerals, College of Geological Sciences&Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Because of its fitness for small sample modeling, a model based on SVM was established. The model used for forecasting the horizontal extending length is combined with the actual measured data of 7# coal seam. The tendency, throw and dip angle of fault was the impact actors of the forecasting model. The software package called libsvm based on Matlab was the platform of the model. Taking advantage of the model, three samples in Zhaoguan mine and three samples in Qiuji mine were perfectly predicted. Compared with the result from multiple regression analysis,SVM has a better outcome when the sample is small.