利用改进的自组织网络进行地震属性分析

自组织神经网络结合地震属性技术往往被用于进行地震相的自动识别，但在实际应用中存在着一些问题难以解决：如神经网络的分类识别能力问题、怎样选取地震属性、怎样解决自组织聚类的有序映射等。对Kohonen自组织网络进行了部分改进，并利用灵敏属性分析技术来解决地震属性的选择问题，最后结合自组织聚类参数利用RBF网络对储层参数进行预测，较为有效地提高了地震多属性储层预测精度。

SEISMIC ATTRIBUTE ANALYSIS BY USING IMPROVED SELF-ORGANIZING NETWORK

The self-organizing neural network technology，combining with seismic attribute，is often used in the automatic identification of seismic facies，but in practical applications，this method has some problems to solve difficultly，such as the classification and recognition of neural network，the selection of seismic attributes and the resolution of the Clustering self-organizing image in an orderly manner and so on.In this paper，some improvement on Kohonen self-organizing networks is made，and the sensitive attribute analysis techniques are used to solve the problem of the choose of seismic attributes，and finally，combining self-organizing network clustering parameters，using of RBF reservoir parameters，the multi-attribute reservoir prediction accuracy is predicted and improved more effectively.