sGs UnMix: a web application for spatial prediction and mixture modeling with a case study on volcanic soil CO2 fluxes

Spatial data analysis and prediction are fundamental in geoscience for mapping continuous variables and supporting decision-making. However, traditional geostatistical tools often require programming skills or involve manual, subjective steps. Here, we developed sGs UnMix, an interactive web application that simplifies spatial prediction workflows and reduces subjectivity in statistical analysis, making it accessible to the entire geoscience community. sGs UnMix (available online at https://apps.bo.ingv.it/sgs-unmix ) is built with the shiny package for R and is organized into four main panels, which allow data loading and coordinate projection, data separation through mixture modeling, variogram modeling, and spatial prediction using sequential Gaussian simulation (sGs). Automated variogram fitting and mixture modeling reduce user bias, while dynamically updated heat maps enable real-time visualization of spatial patterns. sGs UnMix provides not only a standardized approach for estimating volcanic volatile fluxes (e.g., soil CO 2 emissions) but also applications in ore deposit mapping, hydrocarbon exploration, environmental monitoring, and climatology. Compared to existing geostatistical tools, it offers automation, interactivity, and a platform-independent, standalone web-based solution for geoscientists. • sGs UnMix simplifies spatial prediction using sGs with no coding required. • A user-friendly GUI and interactive maps and plots favor spatial data analysis. • The app features automated variogram fitting and mixture models, reducing user subjectivity. • Supports applications in volcanology, environmental science, climatology, oil and gas recovery, and beyond. • Freely available online with open-source code written in shiny for R for future updates.