A machine learning approach for slow slip event detection using GNSS time-series

Donoso, Felipe; Yáñez, Vicente; Ortega-Culaciati, Francisco; Moreno, Marcos

doi:10.1016/j.jsames.2023.104680

Journal of South American Earth Sciences

2023

DOI: 10.1016/j.jsames.2023.104680

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A machine learning approach for slow slip event detection using GNSS time-series

Felipe Donoso,

Vicente Yáñez,

Francisco Ortega-Culaciati

et al.

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2024

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Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Data and Machine Learning: A Joint Interpretation with Geodetic Data (GNSS and InSAR)

Cabrera,

Ardid,

Melchor

et al. 2024

Seismological Research Letters

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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.

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Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Data and Machine Learning: A Joint Interpretation with Geodetic Data (GNSS and InSAR)

Cabrera,

Ardid,

Melchor

et al. 2024

Seismological Research Letters

View full text Add to dashboard Cite

show abstract

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A machine learning approach for slow slip event detection using GNSS time-series

Cited by 1 publication

References 59 publications

Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Data and Machine Learning: A Joint Interpretation with Geodetic Data (GNSS and InSAR)

Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Data and Machine Learning: A Joint Interpretation with Geodetic Data (GNSS and InSAR)

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