概率,多传感器喷发预报

IF 4.6 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Y. Behr, A. Christophersen, C. Miller
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引用次数: 0

摘要

利用贝叶斯网络方法,利用多个传感器或数据流的数据建立了火山喷发预测模型。该模型生成的概率预测是可解释的,并且对传感器中断具有弹性。我们将该模型应用于新西兰海岸外的安山岩岛火山Whakaari/White Island,使用地震记录、地震率、CO2、SO2和H2S排放率。在Whakaari/White岛,我们的模型显示,在2013年至2019年记录的三次爆发前几个月到几周,火山爆发的可能性有所增加。我们的模型优于单独使用任何数据集作为即将爆发的指标。虽然是为Whakaari/White Island开发的,但我们的模型可以很容易地适应其他火山,补充现有的依赖单一数据流的预测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Probabilistic, Multi-Sensor Eruption Forecasting

Probabilistic, Multi-Sensor Eruption Forecasting

We developed an eruption forecasting model using data from multiple sensors or data streams with the Bayesian network method. The model generates probabilistic forecasts that are interpretable and resilient against sensor outage. We applied the model at Whakaari/White Island, an andesite island volcano off the coast of New Zealand, using seismic tremor recordings, earthquake rate, and CO2, SO2, and H2S emission rates. At Whakaari/White Island, our model shows increases in eruption probability months to weeks prior to the three explosive eruptions that were recorded between 2013 and 2019. Our model outperforms the use of any of the data sets alone as an indicator for impending eruptions. Although developed for Whakaari/White Island, our model can be easily adapted to other volcanoes, complementing existing forecasting methods that rely on single data streams.

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来源期刊
Geophysical Research Letters
Geophysical Research Letters 地学-地球科学综合
CiteScore
9.00
自引率
9.60%
发文量
1588
审稿时长
2.2 months
期刊介绍: Geophysical Research Letters (GRL) publishes high-impact, innovative, and timely research on major scientific advances in all the major geoscience disciplines. Papers are communications-length articles and should have broad and immediate implications in their discipline or across the geosciences. GRLmaintains the fastest turn-around of all high-impact publications in the geosciences and works closely with authors to ensure broad visibility of top papers.
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