The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support v...The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.展开更多
By inverting fault slip data, the parameters of 12 tectonic stress tensors in the mine region can be determined. The following characteristics can be obtained for recent tectonic stress fields, which are found deep in...By inverting fault slip data, the parameters of 12 tectonic stress tensors in the mine region can be determined. The following characteristics can be obtained for recent tectonic stress fields, which are found deep in the study region. The results show that the recent tectonic stress field mainly presents the characteristics of near NWW-SSE maximum compressional stress and near NE-SW minimum extensional stress, while the stress regimes are mainly of strike slip, part of the reverse-fault type. Recent tectonic stress field in the region is characterized by horizontal components. The maximum principal compression stress direction was from NEE to SEE, the average principal compression stress direction was near NWW-SSE maximum compres- sional stress and near NE-SW minimum extensional. The recent tectonic stress field of the studied area can be controlled by a large tectonic stress area.展开更多
基金Project 072400430420 supported by the Natural Science Foundation of Henan Province
文摘The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.
文摘By inverting fault slip data, the parameters of 12 tectonic stress tensors in the mine region can be determined. The following characteristics can be obtained for recent tectonic stress fields, which are found deep in the study region. The results show that the recent tectonic stress field mainly presents the characteristics of near NWW-SSE maximum compressional stress and near NE-SW minimum extensional stress, while the stress regimes are mainly of strike slip, part of the reverse-fault type. Recent tectonic stress field in the region is characterized by horizontal components. The maximum principal compression stress direction was from NEE to SEE, the average principal compression stress direction was near NWW-SSE maximum compres- sional stress and near NE-SW minimum extensional. The recent tectonic stress field of the studied area can be controlled by a large tectonic stress area.
