A New Bayesian Inference Methodology for Modeling Geochemical Elements in Soil with Covariates. Characterization of Lithium in South Iberian Range (Spain)

Abstract

When the scientific need to model geochemical elements in soil, is using geostatistical methodologies, for instance krigings, but we can use a new possibility with Bayesian Inference. The models for the analysis were specified by the authors and estimated using Bayesian inference for Gauss ian Markov Random Field (GMRF) through the Integrated Nested Laplace Approximation (INLA) algorithm. The results allow us to quantify and assess possible spatial relationships between the distribution of lithium and other possible explanatory elements. Are these other elements significant to the study? We believe the methods outlined here may help to find elements such as lithium, as well as contributing to the prediction and management of new extractions or prospection in a region in order to find each che mical element. The application for the modeling is to study the spatial variation in the distribution of lithium and its relationship to other geochemical elements is analyzed in terms of the different possibilities offered by geographical and environmenta l factors. All in all, Lithium presents many important and meaningful uses and applications such as: ceramics and glass, electrical and electronics standing out lithium ion batteries, as well as a lubricator for greases, in metallurgy, pyrotechnics, air pu rification, optics, organic and polymer chemistry, and medicine. This study aims to examine the distribution of lithium in sediments from the area of Beceite, in the Iberian Range and the Catalan Coastal Range (Catalànids), within the geological context of the Iberian Plate. The Atlas Geoquímico de España (IGME, 2012) was used as the main geochemical data bank in order to carry out a statistical analysis study.