FLUID EVOLUTION AND GEOCHEMICAL SIGNATURE OF MINERAL SPRINGS: A MULTIVARIATE STATISTICAL APPROACH

An integrated multivariate statistical approach was used to understand processes responsible for fluid evolution and spatial relationships among chemical species for seven springs in central and eastern Jamaica. The classification of three distinct clusters using hierarchical cluster analysis (HCA) based on Ward’s method and squared Euclidean distance reflects the diversity in the chemistry of the water from hot, low-temperature thermal, and cold mineral springs. Principal component analysis (PCA), discriminant analysis (DA), hierarchical cluster analysis (HCA), factor analysis (FA), and regression all confirm variations of Na-Cl water types for three clusters in the study region. The first three components of a PCA account for 99.79% of the total variance for B⁺, Cl^-, Li⁺, Sr²⁺ concentrations in thermal waters. Fluid temperature, total dissolved solids (TDS), electrical conductivity (EC), Na⁺, K⁺, Mg²⁺, Cl^-, Br^-, and SO₄^2- were the most significant parameters for the discrimination of seawater mixing in the samples. A strong correlation between high loadings of TDS, Cl^-, SO₄^2- and Br^- and electrical conductivity (EC) is evident of deep saline brines emerging from limestones along faults in north and south central Jamaica. Smaller loadings of TDS, B⁺, Cl^-, and SO₄^2-, decent of meteoritic recharge, dilution of thermal waters, and sulfate reduction are characteristic of water interaction with volcanic rocks in eastern Jamaica. Two principal components were obtained within 71% of total variance for five dominant factor loadings (Na⁺, K⁺, Mg²⁺, Cl^-, B^-,) in one hot spring and two low-temperature thermal water samples. Discriminant analysis performed in standard mode supports the HCA and discriminates the most significant variables associated with the differences among three distinctive groups. Statistical interpretation of the data infer major processes controlling hydrochemistry include ion exchange, dissolution, meteoritic recharge, seawater mixing, hydrothermal alteration, and sulfate reduction. The results of this study demonstrate the usefulness of multivariate statistical analyses in (1) improving the interpretation of hydrogeochemical data not otherwise provided by graphical methods and (2) analyzing relationships between physiochemical parameters in groups of homogeneity.