Volcano-Seismic Transfer Learning and Uncertainty Quantification With Bayesian Neural Networks
Volcano-Seismic Transfer Learning and Uncertainty Quantification With Bayesian Neural Networks
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DOI：
10.1109/tgrs.2019.2941494
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发表时间：
2020-02-01
影响因子：
8.2
通讯作者：
Ibanez, Jesus M.
中科院分区：
文献类型：
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作者：
Bueno, Angel;Benitez, Carmen;Ibanez, Jesus M.
在过去的几年里，深度学习（DL）已经成为火山和地震地震学领域的重要工具。然而，这些方法的应用并没有对相关的不确定性进行彻底的分析。在这里，我们提出了一个解决方案，以加强火山地震监测系统，通过概率贝叶斯DL；我们实现并演示了波形分类的工作流程，相关不确定性的快速量化，并将这些不确定性与火山动荡的变化联系起来。具体来说，我们引入贝叶斯神经网络（BNNs）对美国华盛顿圣海伦斯火山和俄罗斯堪察加半岛贝兹米尼亚尼两座活火山的数据进行事件识别、分类和不确定性估计。我们演示了当两个数据集合并在一起时，bnn如何在区分事件类型及其起源方面获得出色的性能（92.08 & x0025;），并且不提供额外的训练信息。最后，我们证明了bnn学习到的数据表示在不同的喷发时期是可转移的。我们还发现，估计的不确定性与火山不稳定状态的变化有关，并提出它可以用来衡量学习的模型是否可以导出到其他喷发场景。
Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08 & x0025;) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios.