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Volcanic precursor revealed by machine learning offers new eruption forecasting capability
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  • Kaiwen Wang,
  • Felix Waldhauser,
  • Maya Tolstoy,
  • David P. Schaff,
  • Theresa Sawi,
  • William S. D. Wilcock,
  • Yen Joe Tan
Kaiwen Wang
Lamont-Doherty Earth Observatory, Columbia University

Corresponding Author:[email protected]

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Felix Waldhauser
Lamont-Doherty Earth Observatory of Columbia University
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Maya Tolstoy
School of Oceanography, University of Washington
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David P. Schaff
Lamont-Doherty Earth Obs
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Theresa Sawi
Lamont-Doherty Earth Observatory
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William S. D. Wilcock
University of Washington
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Yen Joe Tan
Earth and Environmental Sciences Programme, Faculty of Science, The Chinese University of Hong Kong
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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 methods, revealing mixed frequency signals that emerge from the continuous waveforms about 15 hours 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 novel machine learning algorithms to characterize subtle changes in magmatic processes associated with eruption preparation, offering new possibilities for forecasting Axial's anticipated next eruption. This novel method is generalizable and can be employed to identify similar precursory signals at other active volcanoes.
03 Feb 2024Submitted to ESS Open Archive
10 Feb 2024Published in ESS Open Archive