新集矿区小断层识别方法研究

Study on identification method of small faults in Xinji Mining Area

以新集矿区口孜东矿为例,为了提高小断层解释精度,对三维地震数据体进行方差、曲率等地震属性提取,并利用蚂蚁追踪增强断裂痕迹,对比实际揭露断层资料,发现"方差蚂蚁体"识别区域断层特征较好,曲率属性刻画断裂细节更具优势,利用加权平均属性融合方法生成"方差蚂蚁"+"曲率"融合属性体,来反映断裂特征。经验证,该方法不仅能够有效识别断距为3~5 m的小断层,对断距小于3 m的次生小断层组合也能清晰显示。

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 3 D 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.