R algorithm for automated H2-CO2-O2 soil gas discrimination and target ranking

The SGSA algorithm, implemented in R, automates the detection of anomaly thresholds using an iterative statistical approach. It first projects log-transformed gas concentrations onto probability diagrams (Q-Q plots) against the theoretical quantiles of a standard normal distribution. A segmented regression is then applied to the sorted distribution to mathematically identify the inflection point (slope break), objectively defining the cutoff value that separates the background population from anomalous values.