This article proposes a new volcano seismic signal descriptor for improving the area under the receiver operating characteristic curve (AUC) in the classification of long-period (LP) and volcano-Tectonic (VT) seismic events. It aims to describe a volcanic seismic event from a different and novel point of view that involves image processing techniques instead of classical seismic signal processing strategies, such as frequency or scale analysis. The proposed descriptor allows exploring the seismic signal space for obtaining the determination of the event patterns and, subsequently, the extraction of intensity-, shape-, and texture-based features into a numeric vectorial output for supplying a set of selected machine learning classifiers with different taxonomies. The descriptor was validated on a seismic signal database collected at the Cotopaxi volcano, containing a total of 637 events, including LP, VT, and other types of seismic events (e.g., rockfall or icequakes). An accuracy value of 96% was obtained in the determination of the event patterns using the signal database, while the values of 0.95 and 0.96 were obtained for the AUC when using a feedforward backpropagation artificial neural network classifier on two experimental data sets, containing feature vectors representing signal with and without event overlapping, respectively. The obtained results demonstrate that the proposed descriptor is capable of providing adequate seismic signal representations in a different feature space and that its output provides competitive results in the classification of volcanic seismic events.
|Número de artículo
|Número de páginas
|IEEE Transactions on Geoscience and Remote Sensing
|Publicada - sep. 2020