ALBERT

Description: The joint analysis of multiparametric datasets in geophysics and, in general, in geosciences is often challenging due to the highly different measurement types. During the last decades, data mining techniques have been subject to intense development, which allows the detection and characterizing of “hidden patterns” within complex datasets. One of the most successful and widely used techniques is the Independent Component Analysis (ICA) which allows for identifying spatio-temporal patterns related to independent sources from dense geospatial datasets. In this work, we apply an extension of the ICA named Independent Vector Analysis (IVA) to analyze a multiparametric dataset of spontaneous potential, CO2 and H2S flux and thermal gradient measurement realized in the crater of Mt. Teide (Tenerife, Canary Islands), from 2020 to 2023. While ICA allows studying spatio-temporal patterns of a single quantity, IVA allows dealing with means multiparametric measurements realized on a single point, which means using vector data instead of a simple scalar. The relationship between spontaneous potential and gas emission is well known and testified by numerous case studies. In this work, however, we exploit for the first time this quantitative approach to separate and characterize endogenous and external factors in this dataset. The approach we propose in this work has a broader application to repeated multiparametric geophysical surveys and in combining geophysical datasets with other kinds of data.