By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e...By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e., drillholes containing faults and drillholes without faults. Discriminant functions were established from the values of the five variables using Fisher's approach. Drillholes in the non-mined areas were allocated to one of the two populations by using discriminant functions. The terrenes of each drillhole were divided into 10 sections, above and below a minable coal seam. Each section has 10 layers of rocks. The population to which each drillhole in a section belongs is sorted out and the probability of each drillhole with faults obtained,i.e., a contour map of predicting the probability of faults in coal mining is shown. A comparison with the real distribution of faults shows that the precision of accurately predicting faults is greater than 70 per cent.展开更多
Using geostatistical method, the semi-variation function models of tobacco mosaic virus （TMV） in east-west and north-south directions were established, and the distribution pattern of TMV in large scale space was st...Using geostatistical method, the semi-variation function models of tobacco mosaic virus （TMV） in east-west and north-south directions were established, and the distribution pattern of TMV in large scale space was studied. The results showed that the distribution pattern of the disease in east-west and north-south directions belonged to linear model with abutment, and the spatial distribution pattern within the investigated areas was aggregated model. The spatial correlation distances in east-west and north-south directions were 29.953 4 and 47.813 8 km, and the spatial variabilities were 95.71% and 80.05%, respectively. This indicated that they had strong spatial correlation. Isoline map accessed by Kringing interpolation method could clearly reflect the spatial aggregated model.展开更多
基金Project 40772198 supported by the National Natural Science Foundation of China
文摘By studying different compressive strengths and changes in the characteristics of rocks,five variables were selected to predict faults in coal mines. Drillholes in the mined area were divided into two populations, i.e., drillholes containing faults and drillholes without faults. Discriminant functions were established from the values of the five variables using Fisher's approach. Drillholes in the non-mined areas were allocated to one of the two populations by using discriminant functions. The terrenes of each drillhole were divided into 10 sections, above and below a minable coal seam. Each section has 10 layers of rocks. The population to which each drillhole in a section belongs is sorted out and the probability of each drillhole with faults obtained,i.e., a contour map of predicting the probability of faults in coal mining is shown. A comparison with the real distribution of faults shows that the precision of accurately predicting faults is greater than 70 per cent.
基金Supported by Modern Tobacco Agriculture-Project of Dingzhai Base Unit
文摘Using geostatistical method, the semi-variation function models of tobacco mosaic virus （TMV） in east-west and north-south directions were established, and the distribution pattern of TMV in large scale space was studied. The results showed that the distribution pattern of the disease in east-west and north-south directions belonged to linear model with abutment, and the spatial distribution pattern within the investigated areas was aggregated model. The spatial correlation distances in east-west and north-south directions were 29.953 4 and 47.813 8 km, and the spatial variabilities were 95.71% and 80.05%, respectively. This indicated that they had strong spatial correlation. Isoline map accessed by Kringing interpolation method could clearly reflect the spatial aggregated model.
