دورية أكاديمية

Tensor-Based Learning Framework for Automatic Multichannel Volcano-Seismic Classification

التفاصيل البيبلوغرافية
العنوان: Tensor-Based Learning Framework for Automatic Multichannel Volcano-Seismic Classification
المؤلفون: Antonio Augusto Teixeira Peixoto, Carlos Alexandre Rolim Fernandes, Pablo Eduardo Espinoza Lara, Adolfo Inza, Jerome I Mars, Jean-Philippe Metaxian, Mauro Dalla Mura, Marielle Malfante
المصدر: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4517-4529 (2021)
بيانات النشر: IEEE, 2021.
سنة النشر: 2021
المجموعة: LCC:Ocean engineering
LCC:Geophysics. Cosmic physics
مصطلحات موضوعية: Classification, machine learning (ML), multidimensional signal processing, tensor learning, volcano, volcano-seismic signals, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
الوصف: This article proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionality reduction, and classifier) in a general multidimensional framework for tensor data, joining tensor learning techniques such as the multilinear principal component analysis (MPCA) and the support tensor machine (STM). By exploiting the use of multiple multichannel triaxial sensors, operating simultaneously in two seismic stations, the tensor patterns are constructed as stations × channels × features. The multidimensional structure of the data is then preserved, avoiding the tensor vectorization that often leads to a feature vector with a large dimension, which increases the number of parameters and may cause the “curse of dimensionality.”Moreover, the array vectorization breaks down the multidimensional structure of the data, which usually leads to performance degradation. The results showed a good performance of the proposed multilinear classification system, significantly outperforming its vectorial counterparts. The best result was obtained with the STuM classifier along with the MPCA.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2151-1535
Relation: https://ieeexplore.ieee.org/document/9408367/; https://doaj.org/toc/2151-1535
DOI: 10.1109/JSTARS.2021.3074058
URL الوصول: https://doaj.org/article/6e396025c32043e38b762bf7e8d7c5d7
رقم الأكسشن: edsdoj.6e396025c32043e38b762bf7e8d7c5d7
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21511535
DOI:10.1109/JSTARS.2021.3074058