Abstract This paper explores the use of six different clustering-based methods to classify long-period and volcano-tectonic seismic events and to find possible overlapping signals of non-volcanic origin that could occur at the same time or immediately after the occurrence of volcano-seismic events. According to the explored classifiers space, the BIRCH method with k = 2 was chosen as the best model in the classification of both pure seismic events, reaching a weighted balanced accuracy and accuracy scores of 0.81 and 0.88, respectively. The accuracy result represents a satisfactory and competitive classification performance when compared to the state of the art methods. Besides, the spectral-clustering method with k = 3 was able to classify seismic events with and without overlapped signals of non-volcanic origin, attaining a weighted balanced accuracy score of 0.51. This result was at least 0.18 units higher than the other classifiers. Additionally, the obtained true positive rates of 0.94 corroborated the excellent performance of this classifier to detect seismic events with overlapping. According to the obtained results, it is possible to state that the proposed clustering-based exploration was effective in providing competitive models for both the classification of uncontaminated seismic events as well as for the detection of seismic events with overlapped signals.

中文翻译：

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探索科托帕希火山地震事件的无监督分类

摘要 本文探讨了使用六种不同的基于聚类的方法对长周期和火山构造地震事件进行分类，并寻找可能同时或在火山发生后立即发生的非火山起源的可能重叠信号。地震事件。根据探索的分类器空间，选择k = 2的BIRCH方法作为两种纯地震事件分类的最佳模型，加权平衡精度和精度分数分别达到0.81和0.88。与最先进的方法相比，准确度结果代表了令人满意且具有竞争力的分类性能。此外，k = 3 的光谱聚类方法能够对有和没有非火山起源的重叠信号的地震事件进行分类，获得 0.51 的加权平衡准确度分数。这个结果至少比其他分类器高 0.18 个单位。此外，获得的 0.94 的真阳性率证实了该分类器在检测重叠地震事件方面的出色性能。根据获得的结果，可以说所提出的基于聚类的勘探在为未受污染的地震事件的分类以及具有重叠信号的地震事件的检测提供有竞争力的模型方面是有效的。