DOI: 10.5281/zenodo.17609308
title: Estimation of CO₂ Emissions in Fault Systems at a Global Scale

description: Dataset Description

This dataset contains all geospatial variables used to model global diffuse CO₂ degassing along fault systems. The target variable consists of point‐based CO₂ flux measurements compiled for this study, while all explanatory variables were harmonized to a common 0.10° (~11 km) global grid using reprojection, resampling, or vector-to-raster conversion.

Target Variable





CO₂ Degassing Flow (g m⁻² d⁻¹)A compilation of diffuse soil CO₂ flux measurements from published sources.Compiled for this study.



Explanatory Variables

Each predictor variable originates from a publicly available global dataset and was processed to a uniform 0.10° resolution for machine-learning modeling.





Bouguer Anomaly (mGal)Derived from the World Gravity Map (WGM2012) global Bouguer anomaly grid (2-arcminute resolution), describing variations in subsurface density structure.(Bonvalot et al., 2012)




Heat Flow (mW/m²)Global conductive heat flow model at 0.5° resolution, representing thermal gradients and crustal heat transport.(Lucazeau, 2019)




Distance to Nearest Fault (km)Computed from the GEM Global Active Faults Database, a global vector dataset of active fault traces. Converted to distance-to-line raster.(Styron & Pagani, 2020)




Peak Ground Acceleration (PGA, g)Global seismic hazard model (6 km grid) representing long-term expected ground motion from earthquakes.(Johnson et al., 2023)




Distance to Nearest Mineralization (km)Based on point locations of global mineral occurrences from the Mineral Resources Data System (MRDS). Converted to a distance raster.(Mason et al., 1996)




Distance to Nearest Volcano (km)Distance to Holocene volcanoes from the Volcanoes of the World (v5.2.7) database.(Global Volcanism Program, 2025)




Distance to Nearest Mineral Product (km)Derived from point-based mineral commodity locations in the MRDS record quality dataset.(Schweitzer, 2019)




Permeability (log k m²)Polygon-based global subsurface permeability estimates from the GLHYMPS hydrogeology dataset.(Gleeson et al., 2014)




Porosity (%)Polygon-based porosity estimates from GLHYMPS, converted to raster and expressed as percent.(Gleeson et al., 2014)




Available Water Storage (mm/m)Soil water storage capacity derived from the Global Agro-Ecological Zones database (1 km grid).(Fischer et al., 2008)




Soil pH (H₂O)Surface soil pH layer from the Harmonized World Soil Database (30 arcseconds).(FAO et al., 2012)




Soil Organic Carbon (ton/ha)Global soil organic carbon stock from the HWSD (30 arcseconds).(FAO et al., 2012)




Tree Cover (%)Global vegetation cover from the Geospatial Information Authority of Japan (15 arcseconds).(Geospatial Information Authority of Japan, 2008)




Average Annual Temperature (°C)Soil bioclimatic temperature variable (SBIO1) at 30 arcseconds, representing near-surface thermal conditions.(van den Hoogen et al., 2021)
description: Influenced by tectonic, geophysical, and environmental aspects, the release of carbon dioxide (CO₂) in fault systems is a fundamental component of Earth's carbon cycle. Appreciating their contribution to natural greenhouse gas flow depends on knowing these emissions. We integrated 8678 degassing records with harminized rasters variablesto provide a predictive framework utilizing Machine Learning to estimate CO₂ fluxes in tectonically active areas. Grid search cross-valuation trained and optimized five regression models—Support Vector Regression, K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting. Then, a stacking regulator was used, which tested to have an R² of 0.677, surpassing individual models. To avoid extrapolation into tectonically stable regions, the model was applied pixel by pixel inside an active-tectonic mask that defines the applicability domain of the model, using only globally available raster variables such as Bouguer anomaly, heat flow, peak ground acceleration, mean annual temperature, vegetation cover, soil properties, porosity, and permeability. The resulting map highlights coherent high-flux zones along the Pacific Ring of Fire, the Andes, and other active orogens, confirming that CO₂ degassing is strongly controlled by geodynamic and geothermal processes. The given method provides a repeatable approach for the subsequent research on subsurface degassing and its function in the global carbon cycle, improving the capacity to define natural CO₂ emissions in tectonically active areas.