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

Christophe Reype and Antonin Richard and Madalina Deaconu and Radu-Stefan Stoica. ( 2020 )
in: 2020 RING Meeting, ASGA

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 dataset 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.

Download / Links

BibTeX Reference

@INPROCEEDINGS{REYPE_RM2020,
    author = { Reype, Christophe and Richard, Antonin and Deaconu, Madalina and Stoica, Radu-Stefan },
     title = { Bayesian statistical analysis of hydrogeochemical data using point processes: a new tool for source detection in multicomponent fluid mixtures },
 booktitle = { 2020 RING Meeting },
      year = { 2020 },
 publisher = { ASGA },
  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 dataset 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. }
}