Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand
In this study, the authors investigate the predictability of sudden eruptions, motivated by the 2019 eruption at Whakaari (White Island), New Zealand. The paper proposes a machine learning approach that is able to identify eruption precursors in data streaming from a single seismic station at Whakaa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-17375-2 |
| _version_ | 1818692236012945408 |
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| author | D. E. Dempsey S. J. Cronin S. Mei A. W. Kempa-Liehr |
| author_facet | D. E. Dempsey S. J. Cronin S. Mei A. W. Kempa-Liehr |
| author_sort | D. E. Dempsey |
| collection | DOAJ |
| description | In this study, the authors investigate the predictability of sudden eruptions, motivated by the 2019 eruption at Whakaari (White Island), New Zealand. The paper proposes a machine learning approach that is able to identify eruption precursors in data streaming from a single seismic station at Whakaari. |
| first_indexed | 2024-12-17T12:54:34Z |
| format | Article |
| id | doaj.art-9a10873129be47a1b81e8531c5177be5 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-17T12:54:34Z |
| publishDate | 2020-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-9a10873129be47a1b81e8531c5177be52022-12-21T21:47:30ZengNature PortfolioNature Communications2041-17232020-07-011111810.1038/s41467-020-17375-2Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New ZealandD. E. Dempsey0S. J. Cronin1S. Mei2A. W. Kempa-Liehr3University of AucklandUniversity of AucklandUniversity of AucklandUniversity of AucklandIn this study, the authors investigate the predictability of sudden eruptions, motivated by the 2019 eruption at Whakaari (White Island), New Zealand. The paper proposes a machine learning approach that is able to identify eruption precursors in data streaming from a single seismic station at Whakaari.https://doi.org/10.1038/s41467-020-17375-2 |
| spellingShingle | D. E. Dempsey S. J. Cronin S. Mei A. W. Kempa-Liehr Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand Nature Communications |
| title | Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand |
| title_full | Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand |
| title_fullStr | Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand |
| title_full_unstemmed | Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand |
| title_short | Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand |
| title_sort | automatic precursor recognition and real time forecasting of sudden explosive volcanic eruptions at whakaari new zealand |
| url | https://doi.org/10.1038/s41467-020-17375-2 |
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