摘要
阳泉煤业集团五矿赵家分区水文地质条件复杂,太原组K2灰岩是15煤层上部的主要含水层.由于煤系地层构造复杂,陷落柱比较发育,因此查明K2灰岩的空间展布和含水性,对于深部15煤层的安全开采具有重要意义.电磁勘探方法由于存在体积效应,很难准确预测K2灰岩的空间展布和含水性.与正常灰岩相比,含水灰岩的波阻抗与视电阻率较小,且孔隙度较大.鉴于此,首先利用多参数岩性反演方法,获得K2灰岩的波阻抗信息;然后利用概率神经网络反演方法,获得K2灰岩的孔隙度和视电阻率信息;最后综合波阻抗、孔隙度和视电阻率三种岩性信息,对K2灰岩进行了含水性评价,为煤矿防治水提供了重要的水文地质依据.
The hydrogeology condition of Yangmei minmetals zhaojia partition is very complicated,K2 limestone of Taiyuan group is aquifer in the upper coal seam. Because of the structure of coal measures is complex,and collapse columns are developed,finding out the spatial distribution and water-bearing property of limestone is very important for the safety of deep coal mining. Because of volume effect,electromagnetic prospecting method is difficult to predict the spatial distribution and water-bearing property of K2 limestone.Compared with normal limestone, water-bearing limestone has smaller pseudo-impedance and the apparent resistivity, larger porosity. In view of this,use multi-parameter lithology inversion get K2 limestone 's impedance; second, use probabilistic neural network inversion get K2 limestone 's porosity and apparent resistivity; finally synthesize three kinds of lithological information:impedance, porosity and apparent resistivity, evaluate the K2 limestone ' s water-bearing property, provides important hydrogeological basis for the prevention and control of mine water.
出处
《地球物理学进展》
CSCD
北大核心
2016年第3期1289-1294,共6页
Progress in Geophysics
基金
国家自然科学基金资助项目(u1261202
41430643)资助
关键词
多参数岩性反演
概率神经网络反演
灰岩
含水性
multi-parameter lithology inversion
probabilistic neural network inversion
limestone
water-bearing property