Volcanic eruption forecasting has a data scarcity problem. Most volcanoes erupt rarely — decades to centuries between events. A well-monitored volcano might have seismic data covering one or two eruptions. Training a machine learning model on one or two positive examples is a recipe for overfitting.
Transfer learning across volcanoes (Nature Communications, 2025) solves this by identifying “ergodic” seismic precursors — signal features that change consistently before eruptions across different volcanoes. Using data from 41 eruptions at 24 volcanoes over 73 years, the study finds common precursor patterns transferable between sites: certain frequency ratios, amplitude trends, and event rate accelerations that precede eruptions regardless of the volcano's specific magmatic system.
The counterintuitive implication: a model trained on volcanoes you've never visited may predict eruptions at your volcano better than a model trained on your volcano's sparse local data. The transferred knowledge from 23 other eruption sequences provides a richer representation of pre-eruptive behavior than the one or two sequences your own seismic network recorded.
This works because the physics of magma movement shares common features across eruption types. Rising magma fractures rock (producing seismicity), pressurizes gas (changing frequency content), and deforms the edifice (changing wave propagation). The details differ — basaltic vs. silicic, phreatic vs. magmatic — but the qualitative patterns of increasing rate, changing spectral content, and accelerating deformation are common enough to transfer.
The approach echoes medical diagnostics: a model trained on thousands of patients performs better than a model trained on one patient's history, even though patients differ. The population-level pattern captures what the individual case cannot. For volcanoes as for patients, individual history is useful but insufficient; cross-system experience is where statistical power lives.