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北黄海盆地烃渗漏蚀变带“磁亮点”的识别研究
引用本文:张丽莉,郝天珧,黄晓霞,黄松,秦静欣,涂广红,赵百民. 北黄海盆地烃渗漏蚀变带“磁亮点”的识别研究[J]. 地球物理学报, 2010, 53(6): 1354-1365. DOI: 10.3969/j.issn.0001-5733.2010.06.014
作者姓名:张丽莉  郝天珧  黄晓霞  黄松  秦静欣  涂广红  赵百民
作者单位:1.中国科学院地质与地球物理研究所油气资源研究重点实验室,北京 100029;2.中国科学院遥感应用研究所,北京 100101;3.广州海洋地质调查局,广州 510075;4.国土资源航空遥感中心,北京 100083
基金项目:国家自然科学基金项目,国家高技术研究发展计划(863),国家科技重大专项,国家科技支撑计划项目 
摘    要:北黄海盆地是我国近海海域尚未取得油气勘探突破的盆地之一.在海洋环境中应用海底油气藏的烃渗漏现象寻找油气有利区具有良好应用前景,为了给该区的含油气远景评价及下一步油气勘探缩小靶区提供地球物理依据,本文利用磁法这一经济、有效的油气渗漏异常地球物理判别手段,开展了识别海底烃渗漏引发磁异常的方法研究.给出一种根据“有导师”的模式识别技术,在充分利用磁异常多种数值特征及纹理特征的基础上,提取烃渗漏蚀变带磁异常的方法.通过已知约束信息(如见油井位等)的点、线、面三种基元及其邻近数据网格点组成基类,将其提取的模式或特征向量作为待识别异常匹配或学习的模板,利用加权欧氏距离函数计算待识别异常特征向量与模板向量之间的相似性,进行模式匹配,从而识别出与模板相似程度较高的异常.应用此方法圈定了北黄海盆地的烃渗漏“磁亮点”分布,从“磁亮点”异常区与中生代地层的分布以及地球化学异常(低层大气烃类检测和海底微生物异常)的对应情况来看,表明该识别方法是识别烃渗漏弱磁异常的一种有效手段.对研究区构造特征、磁异常及地球化学异常特征的综合分析表明,位于北黄海研究区东部和北部的“磁亮点”异常区可能是北黄海盆地较好的含油气远景区.

关 键 词:北黄海盆地  烃渗漏  &ldquo  磁亮点&rdquo  识别  油气资源  
收稿时间:2010-01-06

Recognition of hydrocarbon-seepage-related magnetic anomalies in the North Yellow Sea basin
ZHANG Li-Li,HAO Tian-Yao,HUANG Xiao-Xia,HUANG Song,QIN Jing-Xin,TU Guang-Hong,ZHAO Bai-Min. Recognition of hydrocarbon-seepage-related magnetic anomalies in the North Yellow Sea basin[J]. Chinese Journal of Geophysics, 2010, 53(6): 1354-1365. DOI: 10.3969/j.issn.0001-5733.2010.06.014
Authors:ZHANG Li-Li  HAO Tian-Yao  HUANG Xiao-Xia  HUANG Song  QIN Jing-Xin  TU Guang-Hong  ZHAO Bai-Min
Affiliation:1.Key Laboratory of Petroleum Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China;2.Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China;3.Guangzhou Marine Geological Survey, Guangzhou 510075, China;4.China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
Abstract:The North Yellow Sea Basin is one of the basins in offshore waters of China that have not undergone oil and gas exploration breakthrough. It is promising to use the hydrocarbon seepage phenomenon of sea-floor hydrocarbon reservoirs to predict hydrocarbon potentials in sea areas. In order to provide geophysical evidence for hydrocarbon prospect evaluation and further locating of target areas in the basin, we use the magnetic exploration method, one of the most economical and efficient geophysical methods of discriminating hydrocarbon seepage anomalies, and propose a supervised-learning-based pattern recognition method for recognizing magnetic anomalies related to sea-floor hydrocarbon-seepage alteration zones. Making full of both magnitude and textural features of magnetic anomalies, the method takes three kinds of basic elements (including point, polygonal line, and polygon) and their adjacent grid data cells as basic classes representing known constraint information like oil-encountering drill sites. The patterns or characteristic vectors extracted from the basic classes are used as templates to be matched or studied, and the similarity degrees between anomaly feature vectors and template vectors are calculated with the weighting Euclidean Distance function. In this way, the pattern matching is performed and the anomalies most similar to the templates are thus recognized.  We apply the method to delineate alteration-induced aeromagnetic anomaly zones of the North Yellow Sea Basin. From the overlaying result of the anomalous zones with the distribution of Mesozoic strata and some geochemical anomalies (the detection results of anomalous hydrocarbon gases at the lower atmosphere and microbial germs in the subsurface sediments), the method is efficient in recognizing the magnetic anomalies related to alteration zones. The integrated analysis of regional structure, magnetic field, and geochemical anomalies of the study area shows that the seepage alteration-induced anomalous zones located in the eastern and northern part of the area may be promising hydrocarbon prospects in the North Yellow Sea Basin.
Keywords:The North Yellow Sea basin  Hydrocarbon seepage  Recognition of hydrocarbon-induced magnetic anomalies  Oil-gas resources
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