Volcanic Eruption Mass Estimation: A Machine Learning Approach

Traditional volcano monitoring relies on dense ground-based instrumentation (e.g., seismic, gravity, and deformation measurements), which is available for only a small fraction of the world’s volcanoes. While satellite observations can complement these measurements, estimating total erupted mass—a primary metric of eruption magnitude—remains largely a post-eruption task. Consequently, erupted mass has been quantified for only ~100 of the 1,282 known volcanoes.

These limitations motivate the use of machine learning (ML) in volcanic prediction. Unlike traditional approaches, ML can integrate diverse, globally available datasets to generate predictive insights for volcanoes with limited or no time-dependent monitoring. This capability enables proactive risk assessment and hazard planning, offering a scalable, cost-effective, and globally applicable tool for volcanic risk mitigation. By capturing complex nonlinear relationships among static geophysical, petrological, and tectonic parameters, ML allows estimation of eruption magnitude prior to or early in eruptive activity, an outcome infeasible using classical approaches.

To demonstrate this potential, we present a ML framework to forecast a volcano’s potential erupted mass using static geophysical, petrological, and tectonic characteristics, together with eruption history. The model was trained on a dataset of 914 historical eruptions from 101 volcanoes and applied to estimate erupted mass for 135 globally distributed volcanoes active between 1982 and 2024, assuming a representative eruption duration of 225 days.

This approach provides the first global-scale erupted mass estimates that do not rely on high-resolution, time-dependent monitoring data (e.g., time-lapsed gravity, deformation, or seismicity). Feature importance and permutation analyses indicate that predictions are dominated by static geophysical parameters. The most influential predictors are eruption duration, elevation, gravity, and magnetic data. Parameters with intermediate influence include Moho depth, subsurface thermal indicators (e.g., depth to the Curie isotherm at ~580 °C and the lithosphere–asthenosphere boundary at ~1330 °C), dominant rock type, last eruption, and surface heat flow. Volcano landform, eruption type and start date, host crustal type, and tectonic setting exhibit relatively minor predictive influence.

Our results demonstrate that comprehensive, globally available geophysical datasets can robustly constrain erupted mass for medium- to long-duration eruptions (>3 months), while short-duration eruptive behavior may be better captured through detailed historical eruption records.