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Principal Component Analysis in Integrated Canvas

Principal Component Analysis (PCA) is available as a guided workflow in Integrated Canvas. PCA performs a linear transformation on multi-dimensional datasets. This transformation provides a lower-dimensional picture of the data which can often help to identify and delineate features that were otherwise hidden or difficult to discern in the original data.  PCA attributes can be used directly for structural and stratigraphic visualization and interpretation, and can be used as input to other automated solutions such as the FaultTrak utility, Automatic Fault Extraction, or Growing Neural Gas classification (Attribute Clustering).