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Bayesian statistical analysis of hydrogeochemical data using point processes: a new tool for source detection in multicomponent fluid mixtures

Abstract : Hydrogeochemical data may be seen as a point cloud in a multi-dimensional space. Each dimension of this space represents a hydrogeochemical parameter (i.e. salinity, solute concentration, concentration ratio, isotopic composition...). While the composition of many geological fluids is controlled by mixing between multiple sources, a key question related to hydrogeochemical data set is the detection of the sources. By looking at the hydrogeochemical data as spatial data, this paper presents a new solution to the source detection problem that is based on point processes. Results are shown on simulated and real data from geothermal fluids.
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https://hal.archives-ouvertes.fr/hal-02933268
Contributor : Christophe Reype Connect in order to contact the contributor
Submitted on : Tuesday, September 8, 2020 - 11:59:10 AM
Last modification on : Friday, February 4, 2022 - 3:29:44 AM
Long-term archiving on: : Wednesday, December 2, 2020 - 10:34:35 PM

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  • HAL Id : hal-02933268, version 1
  • ARXIV : 2009.04132

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Christophe Reype, Antonin Richard, Madalina Deaconu, Radu S. Stoica. Bayesian statistical analysis of hydrogeochemical data using point processes: a new tool for source detection in multicomponent fluid mixtures. RING Meeting 2020, Sep 2020, Nancy, France. ⟨hal-02933268⟩

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