神经网络在松辽盆地深层火山岩岩性预测中的应用

APPLICATION OF NEUTRAL NETWORK TO PREDICTING DEEP-SEATED VOLCANIC ROCKS IN THE SONGLIAO BASIN

基于松辽盆地深层火山岩岩心分析,通过模型正演分析发现,深层火山岩产生的重、磁异常不是很大,表现为叠加在强背景之上的次级异常。提出了积分—迭代延拓平化曲线新方法来增强火山岩磁异常信息,以达到均衡磁异常,消除深度的影响。通过对松辽盆地区域磁异常的处理与解释,提取反映深层火山岩的磁异常信息,利用斜导数、欧拉反褶积等多种方法圈定了深层火山岩的分布,指出火山岩的视密度、视磁化率与其两者的相关系数是最佳的三参数组合。神经网络模糊识别是判别火山岩岩性的有效方法,应用该方法在井约束下建立判别网络,完成了深层火山岩岩性的识别。应用神经网络判别火山岩岩性的方法对其他地区深层火山岩的预测有一定的参考和借鉴作用。

Forward modeling of the volcanic cores taken from the deep Songliao Basin demonstrates that the gravity and magnetic anomalies of the rocks,which usually are not high,are a kind of subordinate anomalies superimposed on a strong background.In this paper,the integral-iterative continuation flat change curve was selected to enhance the signal of magnetic anomaly of the volcanic rocks,in order to balance the magnetic anomaly so as to eliminate the depth influence.Through the magnetic anomaly processing and interpretation,the information of the magnetic anomaly of the deep-seated volcanic rocks were extracted.Then the methods of oblique derivative,Euler deconvolution and others were adopted to delineate the distribution pattern of the deep volcanic rocks.Results suggest that apparent density,apparent susceptibility and the correlation coefficient of the above two are the best combination of parameters.The neural network fuzzy recognition is an effective method in recognition of volcanic lithology.A well constrained discrimination network was established for identification of the lithology of volcanic rocks.The method could be used as an effective reference for prediction of deep-seated volcanic rocks in other areas