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Volcanic Precursor Revealed by Machine Learning Offers New Eruption Forecasting Capability
Geophysical Research Letters ( IF 4.6 ) Pub Date : 2024-09-30 , DOI: 10.1029/2024gl108631 Kaiwen Wang, Felix Waldhauser, Maya Tolstoy, David Schaff, Theresa Sawi, William S. D. Wilcock, Yen Joe Tan
Geophysical Research Letters ( IF 4.6 ) Pub Date : 2024-09-30 , DOI: 10.1029/2024gl108631 Kaiwen Wang, Felix Waldhauser, Maya Tolstoy, David Schaff, Theresa Sawi, William S. D. Wilcock, Yen Joe Tan
Seismicity at active volcanoes provides crucial constraints on the dynamics of magma systems and complex fault activation processes preceding and during an eruption. We characterize time-dependent spectral features of volcanic earthquakes at Axial Seamount with unsupervised machine learning (ML) methods, revealing mixed frequency signals that rapidly increase in number about 15 hr before eruption onset. The events migrate along pre-existing fissures, suggesting that they represent brittle crack opening driven by influx of magma or volatiles. These results demonstrate the power of unsupervised ML algorithms to characterize subtle changes in magmatic processes associated with eruption preparation, offering new possibilities for forecasting Axial's anticipated next eruption. This analysis is generalizable and can be employed to identify similar precursory signals at other active volcanoes.
更新日期：2024-10-01
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