Latent Class Clustering And Profile Analysis To Uncover Hidden Patterns Within The Mixed Observed Distribution Of Multivariate Fluid Geothermal Geochemistry Data In Indian Hot Springs

This research paper explores the application of Latent Profile Analysis (LPA) focusing on identifying hidden patterns in the mixed observed distribution of multivariate fluid geothermal geochemistry in Indian hot springs using latent class clustering. The study uses robust multivariate statistical techniques to spatially dependent multivariate geothermal geochemistry data from hot springs located in two regions with different geologictectonic settings: a 2400 km-long arcuate belt in the tectonically active Extra-Peninsular Himalayan region and late-Precambrian or Proterozoic mobile belts in the Central Highland in an otherwise stable landmass or shield of Peninsular India. The study addresses the complexities of selecting significant latent classes from a multitude of factors, utilising graphical representations to illustrate the results of profile analysis, which identified five clusters of geochemical characteristics.By employing Latent Class Analysis (LCA), distinct subgroupings emerged, revealing three significant profiles: HCO3-Cl-SO4, Ca-Mg-Na-K, and F-B, each associated with varying geochemical properties. The findings highlight that the red group (F-B) exhibits weakly acidic properties, while the blue group (Ca-Mg-Na-K) is alkaline, and the green group (HCO3-Cl-SO4) is acidic. The comparison of LCA with other statistical methods, such as traditional cluster analysis and factor analysis, underscores LCA's effectiveness in analyzing categorical data and extracting meaningful patterns in heterogeneous geological settings. Ultimately, this study contributes to a deeper understanding of the geochemical diversity of Himalayan, and other Penisular hot springs, emphasizing the utility of LCA in environmental and geological research.