基于SOM和PSO的非监督地震相分析技术  

Unsupervised seismic facies analysis technology based on SOM and PSO

作　　者：张 郑晓东[1] 李劲松[1] 路交通 曹成寅[1] 隋京坤 

机构地区：[1]中国石油勘探开发研究院,北京100083 [2]中石化石油工程地球物理有限公司,北京100029

出　　处：《地球物理学报》2015年第9期3412-3423,共12页Chinese Journal of Geophysics

基　　金：国家重大专项(2011ZX05004-003);国家自然科学基金(40504110)联合资助

摘　　要：地震相分析技术是储层预测的一种重要方法,可以用来描述有利沉积相带的分布规律.传统的地震相聚类分析方法对大数据的处理运算速度较慢,且容易陷入局部极小值,造成聚类分析的结构不准确.本文提出基于自组织神经网络(SOM)和粒子群优化方法(PSO)相结合的地震相分析技术,利用自组织神经网络能够保持原始地震数据的拓扑结构特性的特点,将大量冗余样本压缩为小样本数据,再通过粒子群的全局寻优能力改善K均值聚类的效果.理论模型和实际应用表明该方法能既有效实现数据压缩,又能提供较为准确的全局解,在地震相预测中兼顾计算效率和计算精度.Seismic facies,as the mappable 3Dseismic units composed of groups of reflections whose parameters differ from those of adjacent facies units,represent seismic reflections to macro characteristics of sedimentary facies.Seismic facies analysis technique is to describe and interpret the seismic reflection parameters,such as configuration,continuity,amplitude,and frequency,within the stratigraphic framework of a depositional sequence.As a key step in the seismic interpretation workflow,seismic facies analysis determines so much information on depositional process,environment and ultimately can predict potential reservoir only from seismic data in the absence of well data.When the geological information is incomplete or nonexistent,seismic facies analysis is called non-supervised and is performed through unsupervised learning or clustering algorithms.Although unsupervised seismic facies analysis is an effective technique for reservoir prediction,the big seismic data are processed slowly with the traditional methods.In order to overcome the defects of traditional ways which easily fall into the minimum value and lead to the inaccuracy of the cluster of seismic data,this paper proposes a new method to analyse seismic facies combining the Self-Organizing Map（SOM）and the Particle Swarm Optimization（PSO）.In this paper,we firstly select the sensitive attribute which can reflect the geologicaltarget and normalize the seismic attribute and initialize the SOM network.The reason why we choose SOM is that it can compress a large number of redundant seismic data into a smaller number.As one of the most promising mathematical techniques applied to non-supervised pattern classification,SOM has the characteristics of keeping the topology structure of the original samples.Secondly we will train the seismic attribute one by one in the network,compute the distance between neuron and sample according to Euclidean distance,confirm the optimum matching unit,and update the weight according to renewing criterion.If it reaches to a c

关 键 词：自组织神经网络 粒子群算法 非监督地震相分析 聚类 

分 类 号：P631[天文地球—地质矿产勘探]