基于脉冲耦合神经网络的地震多属性融合方法

Seismic multi-attribute fusion based on pulse coupled neural networks.

针对单一地震属性进行油气储层预测时往往存在多解性问题,提出基于脉冲耦合神经网络(PCNN)的地震多属性融合方法:通过简化PCNN模型,利用PCNN神经元结构很强的非线性处理功能,确定各神经元之间的数据融合系数,进而获得对应神经元的融合数据输出,从而实现了地震多属性的融合。该方法简捷、计算效率高、融合效果好。通过川东北地区多种属性切片数据的应用验证了该方法的合理性和有效性。

This paper presents a method for seismic multi-attributes fusion based on pulse coupled neural networks(PCNN)for solving the multi-solution dilemma in the single-attribute prediction of oil and gas reservoir.First by facilitating PCNN model,we determine the fusion coefficients for all the neurons with the powerful non-linear processing function of neutral network.Then,we obtain the fusion data output of the responding neurons. Finally,the seismic multi-attributes fusion is realized.The salient features of this proposed method contain its succinct structure;its fast calculation and its high quality fusion results.Applications of the clipped multi-attributes data in the northeast Sichuan demonstrate the efficiency and rationality of the method.