Work

Soil Gas Statistical Analysis (H2, CO2, O2)

Year: 2026

Type: article

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18418166

Authors: Lefeuvre, Nicolas, Rumeau, Manon, Thomas, Eric, Gaucher, Eric C.

Institution: Université de Pau et des Pays de l'Adour

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funder: Agence Nationale de la Recherche