Investigating large-scale change in volcanic activity using machine learning

火山爆发预测是火山学的中心目标。对火山系统的持续监测已导致查明在多个时间尺度上发生的周期性行为。在多个社会相关的时间尺度上，以及在包括圣海伦山、苏弗里埃山火山和斯维托在内的几个地方，观察到了这种周期性，并对其进行了很好的表征（Barmin等人，2002年; Lamb等人，2014年），这给了人们预测这种行为的希望。这些行为模式被热情洋溢和爆发性活动之间的变化点所打断。尽管与火山活动有关的观测数据库不断增加（如地震活动、大地测量和气体测量），但目前仍无法近实时地确定这些变化点。我的目标是使用机器学习方法来识别火山数据集中的变点。地震数据集非常适合训练机器学习框架，由于可用数据的密度，我将开始分析跨越多个喷发阶段的地震数据集。对几种机器学习技术进行的试点研究成功地利用支持向量机和逻辑回归方法模拟了火山活动。之后，可以将其他数据集纳入分析，例如气体和变形测量。将结果与对相同数据集的非机器学习分析进行比较，将确定从新技术中获得的关于火山活动模式的见解，此外，关于火山活动何时出现变点的知识对于向火山系统中不断变化的行为的驱动因素提供信息不可或缺。机器学习方法有可能为喷发变点的物理模型提供信息。目前，火山喷发的结束还没有很好的定义。将活动减少错误地认定为喷发结束已被确定为1982年El Chichon喷发期间造成生命损失的原因之一（Tilling，2009年）。因此，使用数据驱动的方法确定喷发结束的时间提供了对时间的独立约束，从而允许对多参数数据集进行更明智的分析。

Eruption forecasting is a central goal of volcanology. Continued monitoring of volcanic systems has resulted in the identification of periodic behaviour occurring on multiple timescales. This cyclicity has been observed and well-characterised on multiple socially relevant timescales, and in several localities including Mount St Helens, Soufriere Hills Volcano and Santaguito (Barmin et al, 2002; Lamb et al, 2014), giving hope that this behaviour could be forecast. These patterns in behaviour are punctuated by change-points between effusive and explosive activity. Despite an ever-increasing bank of observations associated with volcanic activity, (such as seismicity, geodesy and gas measurements) the ability to pinpoint these change-points in near real-time currently remains beyond reach. I aim to use machine learning methods to identify change-points in volcanic datasets. Seismic datasets are ideally suited to training a machine learning framework and due to the density of data available I will begin by analysing seismic datasets which span multiple phases of eruption. A pilot study of several machine learning techniques has successfully modelled volcanic behaviour using support vector machine and logistic regression methods. Later, it would be possible to incorporate other datasets into the analysis such as gas and deformation measurements. Comparison of results with non-machine learning analysis of the same datasets will identify the insights gained from new techniques into patterns of volcanic activity.Moreover, knowledge of when change-points in volcanic activity occur is integral in informing the drivers of changing behaviour in volcanic systems. Machine learning methods have potential for informing physical models of eruption change-points. Currently, the end of a volcanic eruption is poorly defined. The mistaken identification of decreased activity as the end of eruption has been identified as one of the causes of loss of life during the El Chichon eruption in 1982 (Tilling, 2009). Hence, identification of when the end of eruption occurs using data-driven methods provides an independent constraint on timing, thus allowing a more informed approach towards analysis of multi-parameter datasets.