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

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