煤层瓦斯突出危险区综合预测方法  

Comprehensive prediction method of coal seam gas outburst danger zone

作　　者：李冬[1,2] 彭苏萍[1] 杜文凤[1] 邢朕国 李泽辰 LI Dong;PENG Suping;DU Wenfeng;XING Zhenguo;LI Zechen(State Key Lab of Coal Resources and Safe Mining, China University of Mining and Technology （Beijing） , Beijing 100083, China;College of Geoscience and Surveying Engineering, China University of Mining and Technology （ Beijing ） , Beifing 100083, China)

机构地区：[1]中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京100083 [2]中国矿业大学(北京)地球科学与测绘工程学院,北京100083

出　　处：《煤炭学报》2018年第2期466-472,共7页Journal of China Coal Society

基　　金：国家科技重大专项资助项目(2016ZX05066-001);国家自然科学基金煤炭联合基金资助项目(U1261203)

摘　　要：常规的瓦斯突出预测技术,主要从单一角度出发,无法达到多因素影响下的瓦斯突出危险区域预测精度。以某研究区为例,利用基于遗传算法的支持向量机(SVM)网络,预测了瓦斯含量;将孔隙度作为构造煤的判别因子,并通过概率神经网络(PNN)反演方法,得到了构造煤分布情况;介绍了基于自然伽马曲线的拟密度反演方法,获得了煤层顶板岩性情况。综合瓦斯含量、构造煤分布及煤层顶板岩性3个方面特征,建立了一套瓦斯突出危险区域综合预测方法,为判断瓦斯突出危险区提供了理论基础。经过与实际突出位置做验证,预测结果吻合,说明了综合预测方法在此研究区具有较高的准确性。Conventional technology only considers one factor,which cannot achieve the same precision of gas outburst zone as multi-factor prediction methods. Taking an area as an example,the support vector machine（ SVM） network based on genetic algorithm was used to predict the gas content. The porosity was used as the discriminant factor of tectonic coal. Distribution of tectonic coal was obtained by probabilistic neural network（ PNN）. The quasi-density inversion method based on natural gamma curve was in-troduced to obtain the lithology of coal seam roof. Characteristics of gas content,tectonic coal distribution and coal seam roof lithology was comprehensively considered to establish the gas outburst risk area comprehensive fore-casting method,which provided a theoretical basis to determine the gas outburst danger zone. The prediction results were consistent with actual prominent positions,which proved that this comprehensive forecasting method had high accuracy in this study area.

关 键 词：瓦斯突出 瓦斯含量 构造煤 顶板岩性 综合预测 

分 类 号：TD713[矿业工程—矿井通风与安全]