Dissimilarity-based classification of seismic signals at nevado del ruiz volcano
Automatic classification of seismic signals has been typically carried out on feature-based representations. Recent research works have shown that constructing classifiers on dissimilarity representations is a more practical and, sometimes, a more accurate solution for some patternrecognition proble...
- Autores:
-
Orozco, Mauricio
García, Marcelo E.
P.W. Duin, Robert
G. Castellanos, César
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2006
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/34018
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/34018
http://bdigital.unal.edu.co/24098/
- Palabra clave:
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Automatic classification of seismic signals has been typically carried out on feature-based representations. Recent research works have shown that constructing classifiers on dissimilarity representations is a more practical and, sometimes, a more accurate solution for some patternrecognition problems. In this paper, we consider Bayesian classifiers constructed on dissimilarity representations. We show that such classifiers are a feasible and reliable alternative for automaticclassification of seismic signals. Our experiments were conducted on a dataset containing seismic signals recorded by two selected stations of the monitoring network at Nevado del Ruiz Volcano. Dissimilarity representations were constructed by calculating pairwise Euclidean distances and a non-Euclidean measure on the normalized spectra, which is based on the difference in area between spectral curves. Results show that even though Euclidean dissimilarities have advantageous properties, non-Euclidean measures can be beneficial for matching spectra of seismic signals. |
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