Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Jerome I. Mars¹  Marielle Malfante¹  Jean-Philippe Metaxian²  Pablo Eduardo Espinoza Lara³  Adolfo Inza⁴  Carlos Alexandre Rolim Fernandes⁵  Mauro Dalla Mura⁶ 

[1] Department of Image and Signal, Grenoble Institute of Technology, Grenoble, France;Department of Seismic and Volcanoes, Institut de Recherche Pour le D&x00E9;Electrical and Computer Engineering Graduate Program, Universidade Federal do Ceara, Sobral, Brazil;Redes Geofisicas, Instituto Geofisico Del Peru, Lima, Peru;Universidade Federal do Ceara, Sobral, Brazil;veloppement, Marseille, France;

关键词: Artificial intelligence;    empirical mode decomposition;    deconvolution;    time domain analysis;    spectral domain analysis;    cepstral analysis;   

DOI  :  10.1109/JSTARS.2020.2982714

来源: DOAJ

【 摘 要 】

This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral, and cepstral domains, extracted from the EMD of the signals, as well as a set of preprocessing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.

【 授权许可】

Unknown