The quest to identify optimal methodologies for the observation of geological and environmental processes at the Earth's surface and for analyzing related data presents a significant challenge for numerous researchers. The spatial and temporal dimensions of a given process, along with the selected observational scale, can profoundly influence the comprehensive understanding of the phenomenon in question. Additionally, the unique structural characteristics of geochemical data, which detail the composition of the matrices employed, often obscure meaningful relationships among elements, leading to misleading correlations.
The primary objective of this session is to facilitate a comparative analysis of various methods, encompassing both cutting-edge monitoring and data processing techniques, to offer a real-time assessment of the advantages and disadvantages associated with the diverse approaches presented. Researchers utilizing geochemical data for the assessment of the impact of human activities on the environment or for exploration purposes are encouraged to participate in this session.
While studies focusing on individual matrices are welcomed, research that derives insights from integrated plans involving multiple matrices, including biological ones, is particularly sought after.
Contributions that emphasize data processing techniques utilizing multivariate analysis, machine learning, geostatistics, and other spatial or non-spatial analytical methods are especially encouraged, particularly when they address the compositional nature of geochemical data.
The primary objective of this session is to facilitate a comparative analysis of various methods, encompassing both cutting-edge monitoring and data processing techniques, to offer a real-time assessment of the advantages and disadvantages associated with the diverse approaches presented. Researchers utilizing geochemical data for the assessment of the impact of human activities on the environment or for exploration purposes are encouraged to participate in this session.
While studies focusing on individual matrices are welcomed, research that derives insights from integrated plans involving multiple matrices, including biological ones, is particularly sought after.
Contributions that emphasize data processing techniques utilizing multivariate analysis, machine learning, geostatistics, and other spatial or non-spatial analytical methods are especially encouraged, particularly when they address the compositional nature of geochemical data.