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盆地基底岩性的综合地球物理预测方法——以松辽盆地滨北地区基底岩性预测为例

Comprehensive geophysical prediction method of basement lithology—Example of Binbei area,Songliao basin

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【作者】 李成立崔瑞华刘益中

【Author】 LI Cheng-Li~1,CUI Rui-Hua~1,LIU Yi-Zhong~(2,3) 1 Exploration and DeveLopTnent Research Institue of Daqing Oilfield Company Ltd.,Daqing 163712,China 2 Eastern China Geological & Mining Organization for Non-ferrous Metals in Jiangsu Province,Nanjing 210093,China 3 Team(?)814,Eastern China Geological & Mining Organization for Non-ferrous Metals in Jiangsu Province,Zhenjiang 212005,China

【机构】 大庆油田有限责任公司勘探开发研究院江苏省有色金属华东有色地质勘查局江苏省有色金属华东地质勘查局814队

【摘要】 预测盆地基岩岩性不仅合于研究盆地的深部地质结构及盆地的形成演化具有重要的意义,而且也对基岩风化壳油气藏的勘探具有一定的指导作用.本文通过对盆地重、磁异常成因的综合分析,提出了一系列盆地基底岩性综合预测研究的综合地球物理资料处理解释方法技术.指出在地震构造界面的约束下采用重力剥皮技术可以较为可靠地获取基底岩性重力异常并分析了界面密度差对剥皮后基底岩性重力异常的影响,给出了等效密度差的求取方法.分析了基底起伏对基岩岩性磁异常的影响,指出采用"平化曲"将磁异常归化到与基底同一高度,可以有效地提高对基底岩性体的刻画能力.通过综合分析认为:应用基底的相对视密度、相对视磁化率及两者的相关系数可以有效地刻画基底岩性的特征.神经网络是基底岩性判别与分类的有效方法技术.通过对松辽盆地北部滨北地区的基底岩性的综合预测显示了本文系列预测基底岩性方法的有效性,预测结果反映了松辽盆地基底岩性的分布特征.该系列方法技术可为其他盆地的基底地质填图提供了可借鉴的综合预测方法技术.

【Abstract】 Estimating the basement lithology of basin not only is significant for investigating deep geological structure and evolution of basin,but also has instructive role to exploration of the matrix weathering crust reservoir.On the basis of comprehensively analyzing the cause of gravity and magnetic anomalies of basin,in this paper a series of processing and interpretation methods and techniques of integrated geophysical data for estimating the basement lithology were introduced.The formation-separating technique constrained by the seismic structural interface with gravity data can acquire the gravity anomalies caused by basement lithology,which is affected by density difference between interfaces.The method for calculating equivalent-density difference is neatly presented.The plane-to-surface technique can normalize the magnetic anomalies generated by the basement lithology closely related to the relief of basement to the magnetic anomalies with the same height as the basement,which can effectively improve the capacity of characterizing the basement lithology body.Three parameters composed of apparent density and apparent susceptibility and correlation coefficient between them can effectively describe the characteristics of basement lithology.The neural network method can well discriminate and classify the basement lithology.These methods above presented are applied to comprehensively estimate the distribution characteristics of basement lithology of Benbei area in Songliao Basin,which proves that these methods are effective.This series of methods and techniques provide the referential experience for other basins in the basement geolog’ical mapping.

【基金】 国家重点基础研究发展计划(973)项目(2009CB219307)资助
  • 【文献出处】 地球物理学报 ,Chinese Journal of Geophysics , 编辑部邮箱 ,2011年02期
  • 【分类号】P631
  • 【被引频次】11
  • 【下载频次】93
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