Abstract The classification of seismo-volcanic signals is performed manually at La Soufriere Volcano, which is time consuming and can be biased by subjectivity of the operator. We propose here a machine-learning-based model for classification of these signals, to handle large datasets and provide objective and reproducible results. To describe the properties of the signals, we used 104 statistical, entropy, and shape descriptor features computed from the time waveform, the spectrum, and the cepstrum. First, we trained a random forest classifier with a dataset provided by the Observatoire Volcanologique et Sismologique de Guadeloupe that consisted of 845 labeled events that were recorded from 2013 to 2018: 542 volcano-tectonic (VT); 217 Nested; and 86 long period (LP). We obtained an overalll accuracy of 72%. We determined that the VT class includes a variety of signals that cover the VT, Nested and LP classes. After visual inspection of the waveforms and spectral characteristics of the dataset, we introduced two new classes: Hybrid and Tornillo. A new random forest classifier was trained with this new information, and we obtained a much better overall accuracy of 82%. The model is very good for recognition of all event classes, except Hybrid events (67% accuracy, 70% precision). Hybrid events are often considered to be a mix of VT and LP events. This can be explained by the nature of this class and the physical processes that include both fracturing and resonating components with different modal frequencies. By analyzing the feature weights and by training a model with the most important features, we show that a subset of the 14 best features is sufficient to obtain a performance that is close to that of the model with the whole feature set. However, these best features are different from the 13 best features obtained for another volcano in Peru, with only one feature common to both sets of best features. Therefore, the model is not universal and it must be trained for each volcano, or it is too specific to the one station used here.

中文翻译：

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用于自动分类瓜德罗普岛 La Soufrière 火山火山地震活动的机器学习方法

摘要 La Soufriere Volcano 火山地震信号的分类是人工进行的，耗时且容易受到操作者主观性的影响。我们在这里提出了一种基于机器学习的模型来对这些信号进行分类，以处理大型数据集并提供客观且可重复的结果。为了描述信号的特性，我们使用了 104 个统计、熵和形状描述符特征，这些特征是从时间波形、频谱和倒谱计算出来的。首先，我们使用 Observatoire Volcanologique et Sismologique de Guadeloupe 提供的数据集训练了一个随机森林分类器，该数据集由 2013 年至 2018 年记录的 845 个标记事件组成：542 个火山构造（VT）；217 嵌套；和 86 长周期 (LP)。我们获得了 72% 的整体准确率。我们确定 VT 类包括涵盖 VT、嵌套和 LP 类的各种信号。在对数据集的波形和光谱特征进行目视检查后，我们引入了两个新类：Hybrid 和 Tornillo。使用这些新信息训练了一个新的随机森林分类器，我们获得了 82% 的更好的整体准确率。该模型非常适合识别所有事件类别，但混合事件除外（67% 的准确率，70% 的准确率）。混合事件通常被认为是 VT 和 LP 事件的混合。这可以通过此类的性质和包括具有不同模态频率的断裂和共振分量的物理过程来解释。通过分析特征权重并训练具有最重要特征的模型，我们表明，14 个最佳特征的子集足以获得接近具有整个特征集的模型的性能。但是，这些最佳特征与为秘鲁另一座火山获得的 13 个最佳特征不同，两组最佳特征只有一个共同特征。因此，该模型不是通用的，必须针对每个火山进行训练，否则对于此处使用的一个站点过于具体。