Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling pp 103-121 | Cite as

# Principal Component Analysis

## Abstract

Multivariate statistical methods beyond the correlation and covariance analysis include principal component analysis (PCA), factor analysis, discriminant analysis, classification, and various regression methods. Because of the increasing use of neural networks in the recent decades, some classical statistical methods are now less frequently used. However, PCA has been continuously used in both statistical data analysis and machine learning because of its versatility. At the same time, PCA has had several extensions, including nonlinear PCA, kernel PCA, and PCA for discrete data.

This chapter presents an overview of PCA and its applications to geosciences. The mathematical formulation of PCA is reduced to a minimum; instead, the presentation emphasizes the data analytics and innovative uses of PCA for geosciences. More applications of PCA are presented in Chap. 10.

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