Using Principal Component Analysis (PCA) Combined with Multivariate Change-Point Analysis to Identify Brine Layers Based on the Geochemistry of the Core Sediment

Abstract

The underground brine in Southern Laizhou Bay is characterized by its large scale and high concentration, which can affect the distribution and migration of geochemical elements in sediments. Most studies on the brine are based on hydrochemical analysis, with little consideration being given from a geochemical perspective. Principal component analysis (PCA) is a powerful tool for discovering relationships among many elements and grouping samples in large geochemical datasets. However, even after reducing the dimensions through PCA, researchers still need to make judgments about the meaning represented by each principal component. Change-point analysis can effectively identify the points at which the statistical properties change in a dataset. PCA and change-point analysis have their respective advantages in the study of large sets of geochemical data. Based on the geochemical data of the LZ908 core, by combining these two methods, this study identified four elements (U, MgO, Br, and Na2O) related to the action of seawater through PCA; then, multivariate change point analysis was conducted on these elements to detect the depths of different brine layers. The results of the analysis are basically consistent with those of other studies based on the water content, salinity, and other data, thus proving the effectiveness of this method. The combination of these two methods may also lead to novel approaches for related research.