Understanding the timing of eruption end using a machine learning approach to classification of seismic time series
The timing and processes that govern the end of volcanic eruptions are not yet fully understood, and there currently exists no systematic definition for the end of a volcanic eruption. Currently, end of eruption is established either by generic criteria (typically 90 days after the end of visual sig...
主要な著者: | Manley, G, Pyle, D, Mather, T, Rodgers, M, Clifton, D, Stokell, BG, Thompson, G, Londoño, JM, Roman, DC |
---|---|
フォーマット: | Journal article |
言語: | English |
出版事項: |
Elsevier
2020
|
類似資料
-
Machine learning approaches to identifying changes in eruptive state using multi-parameter datasets from the 2006 eruption of Augustine Volcano, Alaska
著者:: Manley, G, 等
出版事項: (2021) -
A deep active learning approach to the automatic classification of volcano-seismic events
著者:: Manley, G, 等
出版事項: (2022) -
Long-range correlations identified in time-series of volcano seismicity during dome-forming eruptions using detrended fluctuation analysis
著者:: Lachowycz, S, 等
出版事項: (2013) -
A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events
著者:: Grace F. Manley, 等
出版事項: (2022-02-01) -
Quiescent-explosive transitions during dome-forming volcanic eruptions: Using seismicity to probe the volcanic processes leading to the 29 July 2008 Vulcanian Explosion of Soufrière Hills Volcano, Montserrat
著者:: Rodgers, M, 等
出版事項: (2016)