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2019年怀卡阿里火山蒸汽爆发的地震前兆可转移至其他火山爆发事件及火山。

Seismic precursors to the Whakaari 2019 phreatic eruption are transferable to other eruptions and volcanoes.

作者信息

Ardid Alberto, Dempsey David, Caudron Corentin, Cronin Shane

机构信息

University of Canterbury, Christchurch, New Zealand.

Université Libre de Bruxelles, Bruxelles, Belgium.

出版信息

Nat Commun. 2022 Apr 20;13(1):2002. doi: 10.1038/s41467-022-29681-y.

DOI:10.1038/s41467-022-29681-y
PMID:35443758
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9021187/
Abstract

Volcanic eruptions that occur without warning can be deadly in touristic and populated areas. Even with real-time geophysical monitoring, forecasting sudden eruptions is difficult, because their precursors are hard to recognize and can vary between volcanoes. Here, we describe a general seismic precursor signal for gas-driven eruptions, identified through correlation analysis of 18 well-recorded eruptions in New Zealand, Alaska, and Kamchatka. The precursor manifests in the displacement seismic amplitude ratio between medium (4.5-8 Hz) and high (8-16 Hz) frequency tremor bands, exhibiting a characteristic rise in the days prior to eruptions. We interpret this as formation of a hydrothermal seal that enables rapid pressurization of shallow groundwater. Applying this model to the 2019 eruption at Whakaari (New Zealand), we describe pressurization of the system in the week before the eruption, and cascading seal failure in the 16 h prior to the explosion. Real-time monitoring for this precursor may improve short-term eruption warning systems at certain volcanoes.

摘要

毫无预警就发生的火山爆发在旅游区和人口密集地区可能是致命的。即便有实时地球物理监测,预测突然爆发仍很困难,因为其前兆难以识别,而且不同火山之间的前兆也会有所不同。在此,我们描述了一种由气体驱动的火山爆发的一般地震前兆信号,它是通过对新西兰、阿拉斯加和堪察加半岛18次记录良好的火山爆发进行相关性分析而确定的。该前兆表现为中频段(4.5 - 8赫兹)和高频段(8 - 16赫兹)震颤带之间的位移地震振幅比,在火山爆发前几天呈现出特征性上升。我们将此解释为形成了一个热液密封层,它能使浅层地下水迅速增压。将该模型应用于2019年新西兰怀特岛火山爆发,我们描述了火山爆发前一周系统的增压情况,以及爆炸前16小时密封层的连锁失效。对这种前兆进行实时监测可能会改善某些火山的短期喷发预警系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/981cf738c9fa/41467_2022_29681_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/70c86c4398a4/41467_2022_29681_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/56a005937e49/41467_2022_29681_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/ddd8f2282a0f/41467_2022_29681_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/981cf738c9fa/41467_2022_29681_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/70c86c4398a4/41467_2022_29681_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/56a005937e49/41467_2022_29681_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/ddd8f2282a0f/41467_2022_29681_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7617/9021187/981cf738c9fa/41467_2022_29681_Fig4_HTML.jpg

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3
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Sci Rep. 2023 Jun 17;13(1):9807. doi: 10.1038/s41598-023-36964-x.
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Deep learning and process understanding for data-driven Earth system science.深度学习与过程理解在数据驱动的地球系统科学中的应用。
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