煤层裂隙探测的地震波形分类技术研究  

Study on seismic waveform classification technique for detection of coal seam fracture

作　　者：张许良 单菊萍[2] 彭苏萍[3] 

机构地区：[1]焦作市安全生产监督管理局,河南焦作454000 [2]河南理工大学资源环境学院,河南焦作454003 [3]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

出　　处：《河南理工大学学报（自然科学版）》2009年第3期266-269,共4页Journal of Henan Polytechnic University（Natural Science）

基　　金：创新研究群体科学基金资助项目(50221402);"十五"国家科技攻关重点项目(2001BA803B0403);国家"973"项目(202CB211707);国家杰出青年基金资助项目(50025413)

摘　　要：研究了煤层裂隙探测的地震波形分类技术原理,即根据地震资料,在一定时窗范围内统计地震波的几何形状、频率、能量变化快慢及各种地震属性,从而在剖面上和平面上划分各种地震属性特征总和相近的区域,通过钻孔和已知裂隙资料的标定,探测煤层裂隙.利用地震波形分类技术对淮南潘三矿煤层裂隙的探测结果表明,这种方法行之有效,可以为矿井瓦斯危险性预测提供科学依据.The principle of seismic waveform classification technique for detection of coal seam fracture was studied. Based on seismic data, geometric shape, frequency, speed of energy change and all kinds of seismic attributes of seismic wave can be counted in time window limits. Thereby the close area of summation of all kinds of seismic attributes character can be ploted at section and peace planes. Then coal seam fracture can be detected after demacation of drill and known frature datum. Through the detection of coal seam fracture in Pansan mine of Huainan mining group company, the results show that seismic waveform classification tech- nique is effective, which offers scientific basis for detection of mine gas danger.

关 键 词：煤层裂隙 地震波形分类技术 人工神经网络 

分 类 号：P618.13[天文地球—矿床学]