Inversión AVA-2D utilizando un algoritmo genético híbrido

dc.contributor.advisorMontes Vides, Luis Alfredospa
dc.contributor.authorGutiérrez Bossa, Erick Enriquespa
dc.contributor.researchgroupGRUPO DE GEOFISICAspa
dc.date.accessioned2020-03-09T20:24:47Zspa
dc.date.available2020-03-09T20:24:47Zspa
dc.date.issued2019-03-10spa
dc.description.abstractSeismic inversion allows making estimates of elastomechanical earth properties from seismic and well data. Genetic algorithms are artificial intelligence techniques used in fitness/error optimization present in seismic inversion. Conventionally, genetic algorithms have been applied in binary strings. We compare and optimize genetic operator such as crossover and mutation in real number, binary number and hybrid (real-binary mixture) coded genetic algorithms. Furthermore, we analyze the incorporation of other artificial intelligence techniques including Fuzzy Logic, Selfadaptiveness, and Simulated Annealing. Finally, field data and synthetic implementations of best genetic algorithms allow to establish advantages and shortcomings presented by each seismic inversion algorithm. Seismic processing preserves amplitudes compensating for attenuation and dispersion, surface oblique incidence, downward and upward reflection loses.spa
dc.description.abstractLa inversión sísmica permite estimar propiedades elastomecánicas del subsuelo a partir de registros sísmicos y de pozo. Los algoritmos genéticos son técnicas de inteligencia artificial empleadas en la optimización del error o ajuste de los registros sísmicos y registros sintéticos presente en la inversión sísmica. Convencionalmente, los algoritmos genéticos han sido estudiados en codificación binaria. Esta investigación aborda la comparación y optimización de operadores de cruce y mutación en algoritmos genéticos de codificación binaria (bits), en rea l(números reales) e híbrida (números reales y cadenas de bits). Además, este trabajo analiza la incorporación de otras técnicas de optimización que incluyen lógica difusa, operadores autoadaptativos y Simulated Annealing. Finalmente, la implementación de cuatro algoritmos genéticos en registros sintéticos y reales permite establecer las ventajas y desventajas de cada algoritmo en inversión sísmica. El procesamiento de los registros reales conserva las amplitudes sísmicas al compensar los efectos físicos de atenuación y dispersión, incidencia oblicua, pérdida por transmisión.spa
dc.description.additionalMagíster en Ciencias - Geofísica. Línea de Investigación: Inversión Sísmicaspa
dc.description.degreelevelMaestríaspa
dc.format.extent86spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/76011
dc.language.isospaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Geocienciasspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseCC0 1.0 Universalspa
dc.rights.spaAcceso abiertospa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subject.ddcCiencias de la tierraspa
dc.subject.proposalInteligencia Artificialspa
dc.subject.proposalArtificial Intelligenceeng
dc.subject.proposalGenetic Algorithmseng
dc.subject.proposalAlgoritmos genéticosspa
dc.subject.proposalSeismic Inversioneng
dc.subject.proposalInversión Sísmicaspa
dc.subject.proposalSelfAdaptivenesseng
dc.subject.proposalAutoadaptabilidadspa
dc.titleInversión AVA-2D utilizando un algoritmo genético híbridospa
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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