Metodología para el reconocimiento de patrones sísmico-volcánicos no estacionarios mediante técnicas de aprendizaje adaptativo

dc.contributor.advisorOrozco-Alzate, Mauricio
dc.contributor.advisorLondoño Bonilla, John Makario
dc.contributor.authorCastro Cabrera, Paola Alexandra
dc.contributor.cvlacPaola Alexandra Castrospa
dc.contributor.googlescholarPaola Alexandra Castro Cabreraspa
dc.contributor.orcidP.A.Castro-Cabrera [0000-0002-4442-0715]spa
dc.contributor.researchgatePaola Alexandra Castro Cabreraspa
dc.contributor.researchgroupGrupo de Control y Procesamiento Digital de Señalesspa
dc.contributor.scopusCastro-Cabrera Paola Alexandra [36717473000]spa
dc.date.accessioned2024-01-30T14:59:09Z
dc.date.available2024-01-30T14:59:09Z
dc.date.issued2023
dc.descriptiongraficas, tablasspa
dc.description.abstractEl monitoreo volcánico constituye una tarea imprescindible en el contexto de prevención y gestión del riesgo; en este sentido, los observatorios vulcanológicos y sismológicos cumplen una misión trascendental en la declaración de alertas tempranas de erupción volcánica. Y dentro de esta labor, la correcta clasificación de la sismicidad representa un insumo indispensable para la interpretación del fenómeno volcánico y la caracterización de dinámicas eruptivas; por tal motivo, es necesario que la clasificación se lleve a cabo de manera ágil y confiable. A través de la sismicidad correctamente etiquetada, los expertos analistas pueden caracterizar los procesos que estarían ocurriendo al interior de un volcán, e identificar precursores de una erupción. Sin embargo, la acertada discriminación de eventos sísmicos suele verse afectada por la migración de fuentes sísmicas, alteraciones en la dinámica de fluidos, cambios en los mecanismos de generación de grietas, entre otras situaciones, que pueden modificar la distribución de probabilidad de los registros sísmicos (cambios de concepto), y por tanto, incrementar la no estacionariedad de estas señales. Durante las últimas dos décadas, en las áreas de Aprendizaje Automático y Reconocimiento de Patrones se han desarrollado múltiples técnicas y herramientas aplicadas a enfoques de representación y clasificación de sismos volcánicos, entre las cuales destacan las redes neuronales, las máquinas de vectores de soporte, los modelos ocultos de Markov, entre otros, enmarcados (incluso) en contexto muy actuales como el Aprendizaje Profundo. En general, los estudios hallados al respecto en el estado del arte muestran resultados optimistas; sin embargo, se detalla que éstos son consecuencia de configuraciones experimentales restrictivas que disminuyen la complejidad del problema de clasificación planteado; una condición común es el uso de datos procedentes de periodos cortos de registro y poco representativos de la actividad volcánica. Esta limitación simula un entorno estacionario donde los modelos predictivos tradicionales funcionan eficazmente, pero que van en detrimento al actuar por un tiempo prolongado cuando los cambios de concepto se hacen evidentes. Siendo notable la necesidad de disponer de sistemas automáticos de clasificación que satisfagan las ``condiciones realistas'' del problema, como requerimiento esencial en la vigilancia volcánica, en esta tesis se propone el desarrollo de una metodología de reconocimiento de patrones sísmicos, a partir de registros de eventos volcánicos, que considere la adaptación de la clasificación a entornos y condiciones realistas y cambiantes. Para ello, se diseñó un modelo de clasificación centrado en el área del aprendizaje adaptativo y basado en aprendizaje incremental (aún no explorados en datos sísmicos), con el cual se trata el paradigma del cambio del concepto, de tal manera que algunas propiedades como la recurrencia continua de datos adquiridos, la naturaleza multiclase de los registros, los efectos geológicos y las restricciones de generalización en la clasificación, sean contempladas, aprovechadas y eventualmente contrarrestadas al momento de hacer la clasificación automática de los sismos (Texto tomado de la fuente)spa
dc.description.abstractVolcanic monitoring is an essential task in the context of prevention and risk management; in this sense, the volcanological and seismological observatories fulfill a transcendental mission in the declaration of early warnings of volcanic eruptions. And within this labor, the correct classification of seismicity represents an indispensable supply for the interpretation of the volcanic phenomenon and the characterization of eruptive dynamics; for this reason, it is necessary to carry out the classification in an agile and reliable manner. Through correctly labeled seismicity, expert analysts may characterize the processes that would be taking place inside a volcano, and identify precursors of an eruption. However, the accurate discrimination of seismic events is usually affected by the migration of seismic sources, alterations in fluid dynamics, changes in crack generation mechanisms, among other situations. These conditions may modify the probability distribution of seismic records (concept drifts), and therefore, strengthen the non-stationarity of these signals. During the last two decades, multiple techniques and tools have been developed in Machine Learning and Pattern Recognition areas, and applied to representation and classification approaches of volcanic earthquakes. Neural networks, support vector machines, hidden Markov models are the most outstanding methods that have even been framed in very current contexts such as Deep Learning. In general, the studies found in this regard in the state of the art show optimistic results, however, they are the consequence of restrictive experimental configurations that decrease the complexity of the posed classification problem. A common condition is data usage from short periods of registration and unrepresentative of the volcanic activity. This limitation simulates a stationary environment where traditional predictive models work effectively, but their performance deteriorates when acting for a long time because concept changes become evident. The need to have automatic classification systems that satisfy the ``realistic conditions'' of the problem becomes evident, as an essential requirement in volcanic monitoring and eruption prediction. Therefore, this thesis proposes the development of a seismic pattern recognition methodology, based on records of volcanic events, which considers the adaptation of the classification to realistic and changing environments and conditions. For this, a classification model focused on the area of adaptive learning and based on incremental learning (not yet explored in seismic data) was designed, with which the concept drift paradigm is treated. This way, some properties such as the continuous arrival of acquired data, the multiclass nature of the records, the geological effects and the generalization restrictions in the classification are considered, exploited and eventually counteracted when automatically classifying the volcanic earthquakes.eng
dc.description.curricularareaEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizalesspa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.description.notesDeclaración. Me permito afirmar que he realizado la presente tesis de manera autónoma y con la única ayuda de los medios permitidos y no diferentes a los mencionados en la propia tesis. Todos los pasajes que se han tomado de manera textual o figurativa de textos publicados y no publicados, los he reconocido en el presente trabajo. Ninguna parte del presente trabajo se ha empleado en ningún otro tipo de tesis.spa
dc.description.researchareaReconocimiento de patronesspa
dc.description.sponsorshipEste trabajo se ha llevado a cabo gracias al patrocinio económico del Programa Nacional de Formación de Investigadores, modalidad Doctorado Nacional, Convocatoria 617, de MINCIENCIAS (antes COLCIENCIAS).spa
dc.format.extentxix, 147 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/85512
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.facultyFacultad de Ingeniería y Arquitecturaspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automáticaspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc600 - Tecnología (Ciencias aplicadas)spa
dc.subject.proposalAprendizaje incrementalspa
dc.subject.proposalCambio de conceptospa
dc.subject.proposalDetección de cambiosspa
dc.subject.proposalFlujo de datosspa
dc.subject.proposalMonitoreo volcánicospa
dc.subject.proposalReconocimiento de Patronesspa
dc.subject.proposalSeñal sísmicaspa
dc.subject.proposalIncremental learningeng
dc.subject.proposalConcept drifteng
dc.subject.proposalChange detectioneng
dc.subject.proposalData streameng
dc.subject.proposalVolcanic monitoringeng
dc.subject.proposalPattern recognitioneng
dc.subject.proposalSeismic signaleng
dc.subject.unescoSismologíaspa
dc.subject.unescoVulcanologíaspa
dc.titleMetodología para el reconocimiento de patrones sísmico-volcánicos no estacionarios mediante técnicas de aprendizaje adaptativospa
dc.title.translatedMethodology for the recognition of non-stationary seismic-volcanic patterns using adaptive learning techniqueseng
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
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dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
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dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentPúblico generalspa
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oaire.awardtitleFormación de alto nivel para la ciencia, la tecnología y la innovación (semilleros y jóvenes investigadores, doctorados nacionales y en el exterior)spa
oaire.fundernameMinCiencias (antes Colciencias)spa

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