dc.contributorNitescu, Bogdan
dc.contributorPoveda, Esteban
dc.creatorMontenegro Folleco, Juan Andrés
dc.date.accessioned2023-07-04T21:32:11Z
dc.date.accessioned2023-09-07T01:07:51Z
dc.date.available2023-07-04T21:32:11Z
dc.date.available2023-09-07T01:07:51Z
dc.date.created2023-07-04T21:32:11Z
dc.date.issued2023-06-02
dc.identifierhttp://hdl.handle.net/1992/68094
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8728117
dc.description.abstractEl marco geodinámico de Colombia se caracteriza por una interacción compleja entre tres placas tectónicas: la Sudamericana, la de Nazca y la del Caribe, junto con la interacción entre el bloque Panamá-Choco y el bloque norte de los Andes. Estas interacciones dan lugar a zonas sísmicas con una marcada agrupación, como el enjambre sísmico de Cauca, Murindo y Bucaramanga, cada una de las cuales presenta propiedades geodinámicas distintas. Consecuentemente, lograr una caracterización temprana de los eventos sísmicos asume una importancia crítica; sin embargo, el limitado número de estaciones disponibles en la región plantea un reto significativo en este esfuerzo. En las últimas décadas se ha avanzado considerablemente en el desarrollo y la aplicación de diversas técnicas de aprendizaje automático en el campo de la sismología. Una parte importante de la investigación se ha dedicado aprovechar estos avances para facilitar la localización de terremotos utilizando una única estación. Por tal razón, el presente estudio introduce un sistema sísmico de alerta temprana conocido como E3WS, diseñado específicamente para estimar las magnitudes y localizaciones de terremotos utilizando datos de una única estación. En particular, el sistema E3WS comprende seis modelos entrenados mediante la utilización de técnicas de aprendizaje automático supervisado, concretamente los algoritmos XGBoost y LASSO. Cabe destacar que los resultados obtenidos muestran un comportamiento coherente con las investigaciones anteriores realizadas utilizando el sistema E3WS. En este estudio se utilizó un conjunto de datos compuesto por 110 registros sísmicos comprendidos entre 2016 y 2023, obtenidos de las estaciones HEL y PAL, que son estaciones sísmicas del Servicio Geológico Colombiano ubicadas en las proximidades de los clústeres de Murindo y Cauca, respectivamente. Para evaluar la precisión de las estimaciones, se realizó una comparación entre las estimaciones derivadas del sistema E3WS y los eventos sísmicos listados en el catálogo sísmico del Servicio Geológico. Los resultados revelan que, en el caso del clúster de Murindo, los eventos sísmicos mostraron errores absolutos medios de 0.24 en la estimación de la magnitud, 12.66 km en la estimación de la distancia, 16.53 km en la estimación de la profundidad y 57.07° en la estimación del retroazimut. Sin embargo, en el caso del cluster Cauca, los errores aumentaron significativamente debido a factores asociados al sistema, resultando en errores absolutos medios de 0.34 para la magnitud, 47.14 km para la distancia, 6872 km para la profundidad y 90.34° para la estimación del retroazimut. Además, el estudio llevó a cabo una evaluación de diversos factores que contribuyeron a la amplificación de los errores en los resultados de la estimación.
dc.languagespa
dc.publisherUniversidad de los Andes
dc.publisherGeociencias
dc.publisherFacultad de Ciencias
dc.publisherDepartamento de Geociencias
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dc.rightsAttribution-NoDerivatives 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleEstimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación
dc.typeTrabajo de grado - Pregrado


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