【摘要】 为了解决传统多地震属性的地震相分类方法中"难以引入先验信息用以指导分类,难以给出地震相分类结果可靠程度的定量估计,且各分类参数的权值较难确定"这3个问题,提出了一种新的基于贝叶斯网络的地震相分类方法.该分类方法有效地融合了先验信息和训练样本的分布特征,对提取的多种地震属性进行智能分析,以概率推理的方式得到各地震相类别的概率值,并根据概率分布估计分类结果的可靠程度.详述了贝叶斯网络用于地震相分类的原理与方法,并结合理论地震数据,验证了该方法的可行性和正确性.更多还原

【Abstract】 There are three challenging issues in traditional seismic facies classification based on seismic attributes.Firstly,it is difficult to introduce priori-information into the processing of classification to enhance the result of seismic facies classification.Secondly,it is difficult to quantitatively evaluate reliability of the result for seismic facies classification.Thirdly,it is difficult to determine the weights of all parameters of Bayesian networks in classification.In order to solve the above-mentioned problems,this paper proposes a new approach of seismic facies classification based on Bayesian networks,which effectively combines the priori-information and probability distribution of the training samples to construct a reasonable classification model,and deduce the probability for each of seismic facies.According to the probability distribution of each seismic facies,we could estimate the reliability of the classification results in a quantitative manner.The principles and workflow are presented in detail for applying Bayesian networks to seismic facies classification.The numerical experiment proves that this method is correct and feasible.更多还原