Abstract:
<jats:title>Abstract</jats:title>
<jats:p>Anticipating volcanic eruptions remains a challenge despite significant scientific advancements, leading to substantial human and economic losses. Traditional approaches, like volcano alert levels, provide current volcanic states but do not always include eruption forecasts. Machine learning (ML) emerges as a promising tool for eruption forecasting, offering data-driven insights. We propose an ML pipeline using volcano-seismic data, integrating precursor extraction, classification modeling, and decision-making for eruption alerts. Testing on six Copahue volcano eruptions demonstrates our model’s ability to identify precursors and issue advanced warnings pseudoprospectively. Our model provides alerts 5–75 hr before eruptions and achieving a high true negative rate, indicating robust discriminatory power. Integrating short- and long-term data reveals seismic sensitivity, emphasizing the need for comprehensive volcanic monitoring. Our approach showcases ML’s potential to enhance eruption forecasting and risk mitigation. In addition, we analyze long-term geodetic data (Interferometric Synthetic Aperture Radar and Global Navigation Satellite System) to assess Copahue volcano deformation trends, in which we notice an absence of noteworthy deformation in the signals associated with the six small eruptions, aligning with their small magnitude.</jats:p>
