The accurate labeling of the volcanic earthquake signals is a crucial task in order to estimate the increase in volcanic activity (among other parameters), which contributes to determine a state of volcanic unrest (as a possible precursor of an eruption). Several automatic classification approaches have been proposed in different computer science areas in order to complement the exhaustive human task of manually labeling volcano-seismic events. However, most of these approaches have been designed under the assumption of stationarity; that is, by discarding the changing nature of the volcanic phenomenon that evolves over time. In this work, an adaptive classification strategy based on incremental learning is proposed, which identifies and learns concept drifts of data streams coming from seismic recordings. The proposed strategy uses a classifier ensemble in order to handle recurrent states and classifies data even while true labels are not yet available. Unlike usual experimental protocols in the state–of–the–art, we carried out experiments with seismic data from Villarrica volcano, considering the chronological order of the data during a long period (more than 4 years of registration) to achieve the detection of drifts. The results show that the proposed classification strategy satisfactorily counteracts the changes, in contrast to standard classifiers trained within a traditional learning scheme. The proposed strategy keeps a stable classification accuracy and achieves a reduction of up to 0.28 of the absolute error in episodes of abrupt changes.

为了估计火山活动的增加（以及其他参数），准确标记火山地震信号是一项至关重要的任务，这有​​助于确定火山的动荡状态（可能是火山爆发的前兆）。在不同的计算机科学领域中已经提出了几种自动分类方法，以补充手动标记火山地震事件的详尽人类任务。但是，大多数方法都是在平稳性的前提下设计的。也就是说，通过丢弃随时间演变的火山现象的变化性质。在这项工作中，提出了一种基于增量学习的自适应分类策略，该策略可以识别和学习来自地震记录的数据流的概念漂移。所提出的策略使用分类器集成来处理循环状态并对数据进行分类，即使真正的标签尚不可用。与现有技术中的常规实验方案不同，我们使用比利亚里卡火山的地震数据进行了实验，考虑了长期（超过4年的记录）数据的时间顺序以实现对漂移的检测。结果表明，与传统学习方案中训练的标准分类器相比，所提出的分类策略可以令人满意地抵消变化。所提出的策略可保持稳定的分类准确性，并在突发变化的事件中将绝对误差减少多达0.28。