期刊论文

Attribute Reduction and Optimization for Massive Seismic Data Based on Principal Component Analysis

中文

地震工程学报

1000-0844

2019

41

3

757-762

10.3969/j.issn.1000-0844.2019.03.757

国家自然科学基金(4004-61772223) 武汉市教育局课题(2017118).

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针对传统的地震数据属性降维优化方法所选取的地震数据属性特征贡献率低导致降维过程计算量大、CPU占用率高等问题,提出一种基于主成分分析的海量地震数据属性降维优化方法。首先根据地震样本特征建立地震数据特征矩阵,把矩阵中的特征进行聚类,运用降序法排列聚类结果,选取前几项数据作为地震数据属性特征选取结果,对其结果评估分类信息量;通过特征积分准则(FSC)修正分类信息量,获取海量地震数据属性特征节点;运用主成分分析方法对地震数据属性特征节点主成分添加标签,确定Fisher判别分析与PCA可变动选择不确定关系,建立半监督降维的全局最优化形式,运用特征值分解计算降维结果,克服海量地震数据...

In view of the problems associated with the traditional optimization method for seismic data attribute reduction, i.e., the large amount of computation required in the reduction process and the high CPU occupancy rate, in this paper, we propose an attribute reduction and optimization method for massive seismic data based on principal component analysis (PCA). First, we establish a feature matrix of the seismic data based on the characteristics of seismic samples. The features in the matrix are then clustered and arranged in descending order. We then select the first few data as the seismic dat...