Acquisition, classification, and analysis of seismic data are crucial tasks in volcano monitoring. The large number of seismic signals that are continuously acquired during the first monitoring stage poses a huge challenge for the human experts that must classify and analyze them. Several automatic classification systems have been proposed in the literature to alleviate such an overwhelming workload, each one characterized by different levels of accuracy, computational complexity, and interpretability. Considering this last perspective, which represents one of the recent key issues in geoscience, it is possible to find many accurate methods (in terms of classification accuracy) which however represent black boxes, not permitting a clear interpretation. On the other hand, there are other approaches, such as those based on support vector machines (SVM), random forests (RF), and K-nearest neighbor (KNN), which permit the interpretation of results, rules, and models at different levels. Among these last techniques, KNN approaches for volcanic signal classification typically do not achieve the satisfactory classification results obtained with RF and SVM. One possible reason is that in this context, the KNN rule has usually been applied in its basic version, not exploiting the different advanced KNN variants that have been introduced in recent years. This paper takes one step along this direction, investigating the suitability of a number of advanced versions of the KNN rule for the problem of classifying seismic-volcanic signals. The usefulness of these rules, in comparison with the original KNN rule as well as other interpretable classifiers, is evaluated within a real-world scenario involving a five-class dataset of seismic signals acquired at the Nevado del Ruiz volcano, Colombia. The results show that the classification accuracy of basic KNN is largely improved by these advanced variants, even surpassing that obtained with other classifiers like RF and SVM.
Advanced KNN Approaches for Explainable Seismic-Volcanic Signal Classification
Bicego, Manuele;
2023-01-01
Abstract
Acquisition, classification, and analysis of seismic data are crucial tasks in volcano monitoring. The large number of seismic signals that are continuously acquired during the first monitoring stage poses a huge challenge for the human experts that must classify and analyze them. Several automatic classification systems have been proposed in the literature to alleviate such an overwhelming workload, each one characterized by different levels of accuracy, computational complexity, and interpretability. Considering this last perspective, which represents one of the recent key issues in geoscience, it is possible to find many accurate methods (in terms of classification accuracy) which however represent black boxes, not permitting a clear interpretation. On the other hand, there are other approaches, such as those based on support vector machines (SVM), random forests (RF), and K-nearest neighbor (KNN), which permit the interpretation of results, rules, and models at different levels. Among these last techniques, KNN approaches for volcanic signal classification typically do not achieve the satisfactory classification results obtained with RF and SVM. One possible reason is that in this context, the KNN rule has usually been applied in its basic version, not exploiting the different advanced KNN variants that have been introduced in recent years. This paper takes one step along this direction, investigating the suitability of a number of advanced versions of the KNN rule for the problem of classifying seismic-volcanic signals. The usefulness of these rules, in comparison with the original KNN rule as well as other interpretable classifiers, is evaluated within a real-world scenario involving a five-class dataset of seismic signals acquired at the Nevado del Ruiz volcano, Colombia. The results show that the classification accuracy of basic KNN is largely improved by these advanced variants, even surpassing that obtained with other classifiers like RF and SVM.
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