Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques
A Support Vector Machine Regression (SVMR) algorithm was applied to calculate the epicenter distance using a ten seconds signal, after primary waves arrive at a seismological station near to Bogota - Colombia. This algorithm was tested with 863 records of earthquakes, where the input parameters were...
- Autores:
-
Ochoa Gutierrez, Luis Hernán
Vargas Jimenez, Carlos Alberto
Niño Vasquez, Luis Fernando
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/68576
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/68576
http://bdigital.unal.edu.co/69609/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
earthquake early warning
support vector machine regression
earthquake
rapid response
epicenter distance
seismic event
seismology
Bogota - Colombia
alerta temprana de terremotos
máquinas de soporte vectorial
terremoto
respuesta rápida
distancia epicentral
evento sísmico
sismología
Bogotá - Colombia
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques |
| title |
Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques |
| spellingShingle |
Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques 62 Ingeniería y operaciones afines / Engineering earthquake early warning support vector machine regression earthquake rapid response epicenter distance seismic event seismology Bogota - Colombia alerta temprana de terremotos máquinas de soporte vectorial terremoto respuesta rápida distancia epicentral evento sísmico sismología Bogotá - Colombia |
| title_short |
Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques |
| title_full |
Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques |
| title_fullStr |
Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques |
| title_full_unstemmed |
Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques |
| title_sort |
Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques |
| dc.creator.fl_str_mv |
Ochoa Gutierrez, Luis Hernán Vargas Jimenez, Carlos Alberto Niño Vasquez, Luis Fernando |
| dc.contributor.author.spa.fl_str_mv |
Ochoa Gutierrez, Luis Hernán Vargas Jimenez, Carlos Alberto Niño Vasquez, Luis Fernando |
| dc.subject.ddc.spa.fl_str_mv |
62 Ingeniería y operaciones afines / Engineering |
| topic |
62 Ingeniería y operaciones afines / Engineering earthquake early warning support vector machine regression earthquake rapid response epicenter distance seismic event seismology Bogota - Colombia alerta temprana de terremotos máquinas de soporte vectorial terremoto respuesta rápida distancia epicentral evento sísmico sismología Bogotá - Colombia |
| dc.subject.proposal.spa.fl_str_mv |
earthquake early warning support vector machine regression earthquake rapid response epicenter distance seismic event seismology Bogota - Colombia alerta temprana de terremotos máquinas de soporte vectorial terremoto respuesta rápida distancia epicentral evento sísmico sismología Bogotá - Colombia |
| description |
A Support Vector Machine Regression (SVMR) algorithm was applied to calculate the epicenter distance using a ten seconds signal, after primary waves arrive at a seismological station near to Bogota - Colombia. This algorithm was tested with 863 records of earthquakes, where the input parameters were an exponential function of waveform envelope estimated by least squares and maximum value of recorded waveforms for each component of the seismic station. Cross validation was applied to normalized polynomial kernel functions, obtaining mean absolute error for different exponents and complexity parameters. The epicenter distance was estimated with 10.3 kilometers of absolute error, improving the results previously obtained for this hypocentral parameter. The proposed algorithm is easy to implement in hardware and can be employed directly in the field, generating fast decisions at seismological control centers increasing the possibilities of effective reactions. |
| publishDate |
2018 |
| dc.date.issued.spa.fl_str_mv |
2018-01-01 |
| dc.date.accessioned.spa.fl_str_mv |
2019-07-03T07:10:36Z |
| dc.date.available.spa.fl_str_mv |
2019-07-03T07:10:36Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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publishedVersion |
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ISSN: 2346-2183 |
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https://repositorio.unal.edu.co/handle/unal/68576 |
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http://bdigital.unal.edu.co/69609/ |
| identifier_str_mv |
ISSN: 2346-2183 |
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https://repositorio.unal.edu.co/handle/unal/68576 http://bdigital.unal.edu.co/69609/ |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.spa.fl_str_mv |
https://revistas.unal.edu.co/index.php/dyna/article/view/68408 |
| dc.relation.ispartof.spa.fl_str_mv |
Universidad Nacional de Colombia Revistas electrónicas UN Dyna Dyna |
| dc.relation.references.spa.fl_str_mv |
Ochoa Gutierrez, Luis Hernán and Vargas Jimenez, Carlos Alberto and Niño Vasquez, Luis Fernando (2018) Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques. DYNA, 85 (204). pp. 161-168. ISSN 2346-2183 |
| dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf |
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Universidad Nacional de Colombia - Sede Medellín - Facultad de Minas |
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Universidad Nacional de Colombia |
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ochoa Gutierrez, Luis Hernán294c1675-bfc7-4900-9f22-e05288f15bcd300Vargas Jimenez, Carlos Albertoc5cb462f-7dab-464b-9234-270906198d9e300Niño Vasquez, Luis Fernando6335a811-aa46-47b5-9e67-5f63e6e25b143002019-07-03T07:10:36Z2019-07-03T07:10:36Z2018-01-01ISSN: 2346-2183https://repositorio.unal.edu.co/handle/unal/68576http://bdigital.unal.edu.co/69609/A Support Vector Machine Regression (SVMR) algorithm was applied to calculate the epicenter distance using a ten seconds signal, after primary waves arrive at a seismological station near to Bogota - Colombia. This algorithm was tested with 863 records of earthquakes, where the input parameters were an exponential function of waveform envelope estimated by least squares and maximum value of recorded waveforms for each component of the seismic station. Cross validation was applied to normalized polynomial kernel functions, obtaining mean absolute error for different exponents and complexity parameters. The epicenter distance was estimated with 10.3 kilometers of absolute error, improving the results previously obtained for this hypocentral parameter. The proposed algorithm is easy to implement in hardware and can be employed directly in the field, generating fast decisions at seismological control centers increasing the possibilities of effective reactions.Se aplicó un algoritmo de máquinas de vector de soporte para calcular la distancia epicentral utilizando una señal de diez segundos, después del arribo de ondas primarias a una estación sismológica cercana a Bogotá - Colombia. Este algoritmo fue probado con 863 registros de terremotos donde los parámetros de entrada fueron una función exponencial de la envolvente estimada para los mínimos cuadrados y el valor máximo de las formas de ondas registradas en cada componente de la estación sísmica. Validación cruzada fue aplicada a funciones kernel polinomiales normalizadas, obteniendo la media del error absoluto para diferentes exponentes y parámetros de complejidad. La distancia epicentral se estimó con 10.3 kilómetros de error absoluto, mejorando los resultados previamente obtenidos para este parámetro hipocentral. El algoritmo propuesto es fácil de implementar y puede ser empleado directamente en campo, generando decisiones rápidas en centros de control sismológico incrementado posibilidades de tener reacciones efectivas.application/pdfspaUniversidad Nacional de Colombia - Sede Medellín - Facultad de Minashttps://revistas.unal.edu.co/index.php/dyna/article/view/68408Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaOchoa Gutierrez, Luis Hernán and Vargas Jimenez, Carlos Alberto and Niño Vasquez, Luis Fernando (2018) Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques. DYNA, 85 (204). pp. 161-168. ISSN 2346-218362 Ingeniería y operaciones afines / Engineeringearthquake early warningsupport vector machine regressionearthquakerapid responseepicenter distanceseismic eventseismologyBogota - Colombiaalerta temprana de terremotosmáquinas de soporte vectorialterremotorespuesta rápidadistancia epicentralevento sísmicosismologíaBogotá - ColombiaFast estimation of earthquake epicenter distance using a single seismological station with machine learning techniquesArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL68408-376282-1-PB.pdfapplication/pdf1489049https://repositorio.unal.edu.co/bitstream/unal/68576/1/68408-376282-1-PB.pdfbb7798d5011f27840b97405c6b56ef64MD51THUMBNAIL68408-376282-1-PB.pdf.jpg68408-376282-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg9706https://repositorio.unal.edu.co/bitstream/unal/68576/2/68408-376282-1-PB.pdf.jpgb0525799f60fa20490c64f45e85afc23MD52unal/68576oai:repositorio.unal.edu.co:unal/685762023-06-04 23:03:19.533Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |