[1] In this paper, classification of a large hydrochemical data set (more than 600 water samples and 11 hydrochemical variables) from southeastern California by fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques is performed and its application to hydrochemical facies delineation is discussed. Results from both FCM and HCA clustering produced cluster centers (prot...

Groundwater chemistry data are normally scarce in remote inland areas. Effective statistical approaches are highly desired to extract important information about hydrochemical processes from the limited data. This study applied a clustering approach based on the Gaussian Mixture Model (GMM) to a hydrochemical dataset of groundwater collected in the middle Heihe River Basin (HRB) of northwestern...

Deep (>300 m) subsurface hydrochemical data are important components of geological investigations of potential radioactive waste disposal sites. These data can be interpreted to yield information that supports conceptual hydrogeological models, constrains palaeohydrogeological evolution and characterises subsurface hydrochemical conditions of a site. The most important sources of these data are...

in this study multivariate statistical analysis are used to characterize relationships between hydrochemical properties ofthermal and non-thermal springs. four factors for thermal waters and two for non-thermal springs were extracted basedon factor analysis. in thermal springs, the first factor showed high loading on ca, mg, na and k and this factor wasinterpreted as leaching of cations in the ...

s: Asante, J., and D.K., Kreamer, Basin Hydrochemistry and Related Flow Systems: Integrated Data Analytic Methods. GSA Annual Meeting & Exposition, October, 9-12, 2011 (abstract and oral presentation) Asante, J., D.K., Kreamer, and C.L Cross, Enhancing Multivariate Statistical Characterization of Hydrochemical Groundwater Data: A Comparative Analysis of Clustering Methods. GSA Joint Rocky Mount...

in this study multivariate statistical analysis are used to characterize relationships between hydrochemical properties ofthermal and non-thermal springs. four factors for thermal waters and two for non-thermal springs were extracted basedon factor analysis. in thermal springs, the first factor showed high loading on ca, mg, na and k and this factor wasinterpreted as leaching of cations in the ...

q-mode hierarchical cluster (hca) and principal component analysis (pca) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: june, september, and december, along the ain azel aquifer, algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the h...

The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial 5 knowledge into the algorithm. The performance of the ...

The river Gacka springs catchment area is located in the Dinaric karst, which is globally known as the locus typicus, or classical Karst. It is composed of four major and several minor karst springs of different discharge rates. The river Gacka springs are characterised by great discharge and exceptional quality, so the catchment area of the river is indicated in the Water Management Strategy (...

Many previous studies have demonstrated use of specific solutes or isotopes as tracers of groundwater flow. We present a technique that uses standard hydrochemical information to create statistically based hydrochemical facies, which are then used as tracers of groundwater flow. This approach reduces potential subjective bias during interpretation of single tracer data in complex systems with m...