周华
ZHOU Hua

• 1:大庆油田有限责任公司勘探开发研究院

摘要(Abstract)：

基于松辽盆地深层火山岩岩心的密度及磁化率数据,通过模型正演分析可以看出,深层火山岩产生的重、磁异常不大,大多表现为叠加在强背景之上的次级异常,不易识别火山岩体。为此提出了积分-迭代延拓平化曲方法,增强火山岩磁异常信息,均衡磁异常,消除深度影响。通过应用该方法进行深层火山岩岩体圈定及岩性识别表明:该方法能有效增强深层火山岩的磁异常信息;磁异常的斜导数、欧拉反褶积能有效圈定火山岩边界;重、磁反演的视密度、视磁化率及两者的相关系数是识别火山岩的最佳三参数组合;井约束下的神经网络能有效判别火山岩岩性。
Based on the data of the core density and magnetic susceptibility of the deep volcanic rocks in Songliao Basin and moreover through the forward analyses of the model,the gravity and magnetic anomalies of the rocks are not so serious,they show subordinate anomahes that are superimposed on the strong background and result in the difficulty in the recognition of the volcanic rock bodies.Therefore a new method of integral-iterative continuation changed from flat to curve is put forward to enhance and balance the magnetic anomaly information of the volcanic rocks,and finally eliminate the influences of the depth.By means of the application of the method,the delineation of the rock bodies and identification of the lithologies of the deep volcanic rocks show that the approach can effectively enhance the magnetic anomaly information;the oblique derivative,Euler deconvolution of the anomaly can effectively delineate the boundary of the rocks;the apparent density,magnetic susceptibility obtained from the gravity and magnetic reversion and their correlation coefficients are the best three combination parameters;the neural network constrained by wells is an effective method to recognize the lithologies of the volcanic rocks.

关键词(KeyWords)： 深层火山岩;火山岩磁异常;视密度;视磁化率;神经网络;松辽盆地
deep volcanic rock;volcanic rock magnetic anomaly;apparent density;apparent magnetic susceptibility;neural network;Songliao Basin

Abstract:

Keywords:

基金项目(Foundation): 国家重点基础研究发展计划“973”项目(2009CB219307)

作者(Author): 周华
ZHOU Hua

参考文献(References)：

• [1]董焕成.重磁勘探教程[M].北京:地质出版社,1993:35-151.
• [2]杨文采,施志群,侯遵泽,等.离散小波变换与重力异常多重分解[J].地球物理学报,2001,44(4):535-540.
• [3]程乾生.信号数字处理的数学原理[M].北京:石油工业出版社,1979:35-42.
• [4]Cianciara B,Marcar H Z.Interpretation of gravity anomalies by means of local power spectra[J].Geophysics Prospect,1975,24(3):273-286.
• [5]徐世浙.位场延拓的积分—迭代法[J].地球物理学报,2006,49(4):1176-1182.
• [6]席道瑛,张涛.神经网络模型在测井岩性识别中的应用[J].煤田地质与勘探,1994,22(6):56-61.
• [7]姜传金,冯肖宇,詹怡捷,等.松辽盆地北部徐家围子断陷火山岩气藏勘探新技术[J].大庆石油地质与开发,2007,26(4):135-137.
• [8]蒙启安,门广田,张正和.松辽盆地深层火山岩体、岩相预测方法及应用[J].大庆石油地质与开发,2001,20(3):21-24.
• [9]杨懋新.松辽盆地断陷期火山岩的形成及成藏条件[J].大庆石油地质与开发,2002,21(5):15-17.
• [10]任延广,朱德丰,万传彪,等.松辽盆地徐家围子断陷天然气聚集规律与下步勘探方向[J].大庆石油地质与开发,2004,23(5):26-29.
• [11]郑常青,王璞珺,刘杰,等.松辽盆地白垩系火山岩类型与鉴别特征[J].大庆石油地质与开发,2007,26(4):10-14