Spatial Classification of Coastal Depositionary Environments Using Spatial Correlation, and the Distribution Characteristics of Organic Matter Including Total Organic Carbon (TOC)

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The research aims to classify coastal depositionary environments using machine learning and analyze organic matter distribution characteristics.
Utilized data from a national monitoring program of surface sediments in coastal areas.
Applied Gaussian Mixture Model (GMM) clustering to identify four distinct sediment environments.
Conducted spatial correlation analysis using semivariograms to evaluate the spatial structure of clusters.
Examined distribution characteristics of organic indicators like TOC, IL, COD, and AVS by cluster.
Identified four sediment environments: Cluster 1 (zS) and Clusters 2-4 (sZ).
The TOC levels ranged from 0.4% in Cluster 1 to 1.3% in Cluster 3, correlating with sediment grain size.
Clusters 1 and 3 showed a marine origin of organic matter, while Clusters 2 and 4 indicated a significant influence of terrestrial organic matter.
A significant difference in organic indicators was noted across clusters with p < 0.001.

Research unveils sediment environment classification in coastal areas, highlighting implications for marine management.

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Kim et al. (2026) studied this question.

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