Machine learning approaches to identifying changes in eruptive state using multi-parameter datasets from the 2006 eruption of Augustine Volcano, Alaska
Understanding the timing of critical changes in volcanic systems, such as the beginning and end of eruptive behaviour, is a key goal of volcanic monitoring. Traditional approaches to forecasting these changes have used models motivated by the underlying physics of eruption onset, which assume that g...
| 主要な著者: | , , , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
American Geophysical Union
2021
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