Abstract 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 signals of eruption) or criteria specific to a given volcano. We explore the application of supervised machine learning classification methods: Support Vector Machine, Logistic Regression, Random Forest and Gaussian Process Classifiers and define a decisiveness index D to evaluate the consistency of the classifications obtained by these models. We apply these methods to seismic time series from two volcanoes chosen because they display contrasting styles of eruption: Telica (Nicaragua) and Nevado del Ruiz (Colombia). We find that, for both volcanic systems, the end-date we obtain by classification of seismic data is 2–4 months later than end-dates defined by the last occurrence of visual eruption (such as ash emission). This finding is in agreement with previous, general definitions of eruption end and is consistent across models. Our classifications have a higher correspondence of eruptive activity with visual activity than with database records of eruption start and end. We analyze the relative importance of the different features of seismic activity used in our models (e.g. peak event amplitude, daily event counts) and find little consistency between the two volcanic systems in terms of the most important features which determine whether activity is eruptive or non-eruptive. These initial results look promising and our approach may offer a robust tool to help determine when an eruption has ended in the absence of visual confirmation.

中文翻译：

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使用机器学习方法对地震时间序列进行分类，了解喷发结束的​​时间

摘要 控制火山喷发结束的​​时间和过程尚不完全清楚，目前还没有对火山喷发结束的​​系统定义。目前，喷发结束是通过通用标准（通常是喷发视觉信号结束后 90 天）或特定火山的特定标准来确定的。我们探索了监督机器学习分类方法的应用：支持向量机、逻辑回归、随机森林和高斯过程分类器，并定义了一个决定性指数 D 来评估这些模型获得的分类的一致性。我们将这些方法应用于选择的两个火山的地震时间序列，因为它们显示出对比鲜明的喷发风格：Telica（尼加拉瓜）和 Nevado del Ruiz（哥伦比亚）。我们发现，对于两个火山系统，我们通过地震数据分类获得的结束日期比最后一次目视喷发（如灰烬排放）定义的结束日期晚 2-4 个月。这一发现与之前对喷发结束的​​一般定义一致，并且在模型之间是一致的。我们的分类比喷发开始和结束的数据库记录具有更高的喷发活动与视觉活动的对应关系。我们分析了我们模型中使用的地震活动不同特征的相对重要性（例如峰值事件幅度、每日事件计数），并发现两个火山系统之间在决定活动是喷发还是非喷发的最重要特征方面几乎没有一致性。 - 爆发。