A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

Abstract

Volcano-seismic event classification represents a fundamental component of volcanic monitoring. Recent advances in techniques for the automatic classification of volcano-seismic events using supervised deep learning models achieve high accuracy. However, these deep learning models require a large, labelled training dataset to successfully train a generalisable model. We develop an approach to volcano-seismic event classification making use of active learning, where a machine learning model actively selects the training data which it learns from. We apply a diversity-based active learning approach, which works by selecting new training points which are most dissimilar from points already in the model according to a distance-based calculation applied to the model features. We combine the active learning with an existing volcano-seismic event classifier and apply the model to data from two volcanoes: Nevado del Ruiz, Colombia and Llaima, Chile. We find that models with data selected using an active learning approach achieve better testing accuracy and AUC (Area Under the Receiver Operating Characteristic Curve) than models with data selected using random sampling. Additionally, active learning decreases the labelling burden for the Nevado del Ruiz dataset but offers no increase in performance for the Llaima dataset. To explain these results, we visualise the features from the two datasets and suggest that active learning can reduce the quantity of labelled data required for less separable data, such as the Nevado del Ruiz dataset. This study represents the first evaluation of an active learning approach in volcano-seismology.