Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra-Reference-Cited by-同舟云学术

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Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

Published:2016-06 Issue: Volume:320 Page:58-74
ISSN:0377-0273
Container-title:Journal of Volcanology and Geothermal Research
language:en
Short-container-title:Journal of Volcanology and Geothermal Research

Author:

Unglert K.,Radić V.,Jellinek A.M.

Funder

UBC Four-Year-Fellowship

Vanier Canada Graduate Scholarship

Natural Sciences and Engineering Research Council

Canadian Institute for Advanced Research

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Geophysics

Reference85 articles.

1. Source mechanism of volcanic tremor: fluid-driven crack models and their application to the 1963 Kilauea eruption;Aki;J. Volcanol. Geotherm. Res.,1977

2. Instability in flow through elastic conduits and volcanic tremor;Balmforth;J. Fluid Mech.,2005

3. Pattern recognition and lithological interpretation of collocated seismic and magnetotelluric models using self-organizing maps;Bauer;Geophys. J. Int.,2012

4. New constraints on source processes of volcanic tremor at Arenal Volcano, Costa Rica, using broadband seismic data;Benoit;Geophys. Res. Lett.,1997

5. Pattern Recognition and Machine Learning;Bishop,2006

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