基于主成分分析方法的海量地震数据属性降维优化
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摘要
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,克服海量地震数据属性降维过程中的过拟合问题,融合主成分分析算法与Fisher判别分析算法实现海量地震数据属性降维优化。实验结果证明,所提方法选取的属性特征精度及贡献率较高,降维过程中CPU占用率较低。
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 data attribute feature results and evaluate the classification information of these results. Next, the classification information is modified using the feature integral criterion to obtain the attribute feature nodes of the massive seismic data. We use PCA to label the principal components of the attribute nodes of the seismic data and establish a global optimization of the semi-supervised dimensionality reduction. The dimensionality reduction results are calculated by eigenvalue decomposition, we solved the problem of over-fitting in the attribute reduction process of massive seismic data, and realized the optimization of the attribute reduction of massive seismic data by combining the PCA algorithm with Fisher discriminant analysis. The experimental results show that the proposed method has a high accuracy and contribution rate of attribute feature selection, and the CPU occupation rate is low during the dimensionality reduction process.
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 data attribute feature results and evaluate the classification information of these results. Next, the classification information is modified using the feature integral criterion to obtain the attribute feature nodes of the massive seismic data. We use PCA to label the principal components of the attribute nodes of the seismic data and establish a global optimization of the semi-supervised dimensionality reduction. The dimensionality reduction results are calculated by eigenvalue decomposition, we solved the problem of over-fitting in the attribute reduction process of massive seismic data, and realized the optimization of the attribute reduction of massive seismic data by combining the PCA algorithm with Fisher discriminant analysis. The experimental results show that the proposed method has a high accuracy and contribution rate of attribute feature selection, and the CPU occupation rate is low during the dimensionality reduction process.
引文
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[4] 李万益,孙季丰.基于高斯增量降维与流形Boltzmann优化的人体运动形态估计[J].电子学报,2017,45(12):3060-3069.LI Wanyi,SUN Jifeng.Human Motion Estimation Based on Gaussion Incremental Dimension Reduction and Manifold Boltzmann Optimization[J].Acta Electronica Sinica,2017,45(12):3060-3069.
[5] 黄南天,彭华,蔡国伟,等.电能质量复合扰动特征选择与最优决策树构建[J].中国电机工程学报,2017,37(3):776-785.HUANG Nantian,PENG Hua,CAI Guowei,et al.Feature Selection and Optimal Decision Tree Construction of Complex Power Quality Disturbances[J].Proceedings of the CSEE,2017,37(3):776-785.
[6] 罗乃丽,李霞,王娜.利用冲突信息降维的进化高维目标优化算法[J].信号处理,2017,33(9):1169-1178.LUO Naili,LI Xia,WANG Na.Objective Reduction Using Conflict Information in Many-Objective Optimization Evolutionary Algorithm[J].Journal of Signal Processing,2017,33(9):1169-1178.
[7] 周红标,乔俊飞.基于高维k-近邻互信息的特征选择方法[J].智能系统学报,2017,12(5):595-600.ZHOU Hongbiao,QIAO Junfei.Feature Selection Method Based on High Dimensional K-Nearest Neighbors Mutual Information[J].CAAI Transactions on Intelligent Systems,2017,12(5):595-600.
[8] 姚晟,徐风,赵鹏,等.基于邻域量化容差关系粗糙集模型的特征选择算法[J].模式识别与人工智能,2017,30(5):416-428.YAO Sheng,XU Feng,ZHAO Peng,et al.Feature Selection Algorithm Based on Neighborhood Valued Tolerance Relation Rough Set Model[J].Pattern Recognition and Artificial Intelligence,2017,30(5):416-428.
[9] 祝璞,黄章进.基于稀疏贝叶斯模型的特征选择[J].计算机工程,2017,43(4):183-187,193.ZHU Pu,HUANG Zhangjin.Feature Selection Based on Sparse Bayesian Model[J].Computer Engineering,2017,43(4):183-187,193.
[10] 刘航.考虑不确定性的地震灾害应急救援设备并行优化设计[J].地震工程学报,2018,40(5):1118-1123.LIU Hang.Parallel Optimization Design,Incorporating Uncertainty,of Emergency Rescue Equipment for Earthquake Disasters[J].China Earthquake Engineering Journal,2018,40(5):1118-1123.