Study on identification method of small faults in Xinji Mining Area
Pan Jichuan1， Chen Xinhong2， Li Hongming2
1. China University of Mining and Technology (Beijing) School of Earth Science and Surveying and Mapping Engineering , Beijing 100000, China; 2. China Coal Xinji Energy Corporation Ltd., Huainan 232000, China
Kouzidong Mine in Xinji Mining Area was taken as an example, in order to improve the interpretation accuracy of small faults, seismic attributes such as variance and curvature were extracted from the 3D seismic data volume, and ant tracking algorithm was used to enhance the fault traces. Compared with the actual exposed fault data, it is found that the "variance ant body" is better on identifying regional fracture features, and the curvature attribute has more advantages on characterizing fracture details. Therefore, a weighted average attribute fusion method was used to generate a "variance ant" + "curvature" fusion attribute body to reflect the fracture characteristics. It was verified that this method can not only effectively identify small faults with a fault distance of 3 to 5 m, but also clearly display the combination of small secondary faults with a distance of less than 3 m.
潘冀川1，陈新宏2，李洪明2. 新集矿区小断层识别方法研究[J]. 煤炭与化工, 2020, 43(7): 75-78.
Pan Jichuan1， Chen Xinhong2， Li Hongming2. Study on identification method of small faults in Xinji Mining Area. CCI, 2020, 43(7): 75-78.
