刘文明,刘万金,裴跟弟
LIU Wenming,LIU Wanjin,PEI Gendi

• 1:中煤科工集团西安研究院有限公司
• 2:中国矿业大学(北京)煤炭资源与安全开采国家重点实验室

摘要(Abstract)：

煤层顶板岩性对于煤矿安全生产产生很大的影响。通过神经网络方法对自然伽马测井与地震属性(包括波阻抗属性)进行训练得到两者的非线性关系,并将其应用到整个地震数据中得到拟自然伽马体。相对于波阻抗属性,自然伽马参数可以很好地区分砂泥岩,从而更直接预测煤层顶板岩性,提高岩性预测的分辨率。
The lithology of coalbed's roof has huge impact on the safe mining. Through training the data of seismic attributes(including P-impedance) and gamma ray logging data by neural network algorithm, we can get the nonlinear relationship of them, then we apply the nonlinear relationship to the whole seismic data volume and get pseudo gamma ray data volume. Compared to P-impedance, the gamma ray data can better distinguish the sandstone and mudstone, therefore, we can predict the lithology of coalbed's roof more directly and accurately, also improve the resolution on lithology prediction issues.

关键词(KeyWords)： 煤层顶板岩性;地震属性;神经网络;自然伽马测井
lithology of coalbed's roof;seismic attributes;neural network;gamma ray logging

Abstract:

Keywords:

基金项目(Foundation): 国家自然基金项目(U1261203);; 国家十二五科技支撑计划(2012BAB13B01;2012BAC10B03)~~

作者(Author): 刘文明,刘万金,裴跟弟
LIU Wenming,LIU Wanjin,PEI Gendi

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