Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo

ilustraciones, mapas + anexo

Autores:: Castillo Taborda, Emmanuel David

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
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repository_id_str
dc.title.spa.fl_str_mv	Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo
dc.title.translated.eng.fl_str_mv	Monitoring seismic activity in the Colombian territory using Deep Learning
title	Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo
spellingShingle	Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 550 - Ciencias de la tierra::558 - Ciencias de la tierra de América del Sur Protección y prevención ante los sismos Predicción sísmica Earthquakes - prevention and protection Earthquake prediction Autopicking PhaseNet EQTransformer Colombian seismicity Deep Learning Aprendizaje Profundo Sismicidad Colombiana Autopicado
title_short	Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo
title_full	Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo
title_fullStr	Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo
title_full_unstemmed	Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo
title_sort	Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo
dc.creator.fl_str_mv	Castillo Taborda, Emmanuel David
dc.contributor.advisor.none.fl_str_mv	Prieto Gómez, Germán Andrés
dc.contributor.author.none.fl_str_mv	Castillo Taborda, Emmanuel David
dc.contributor.datamanager.none.fl_str_mv	Servicio Geológico Colombiano Levander, Alan
dc.contributor.projectmember.none.fl_str_mv	Siervo Plata, Daniel David
dc.contributor.orcid.spa.fl_str_mv	Castillo, Emmanuel [0000-0002-9799-9775]
dc.contributor.cvlac.spa.fl_str_mv	Castillo, Emmanuel [0001730420]
dc.contributor.researchgate.spa.fl_str_mv	Castillo, Emmanuel [https://www.researchgate.net/profile/Emmanuel_Castillo4]
dc.contributor.googlescholar.spa.fl_str_mv	Castillo, Emmanuel
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 550 - Ciencias de la tierra::558 - Ciencias de la tierra de América del Sur
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 550 - Ciencias de la tierra::558 - Ciencias de la tierra de América del Sur Protección y prevención ante los sismos Predicción sísmica Earthquakes - prevention and protection Earthquake prediction Autopicking PhaseNet EQTransformer Colombian seismicity Deep Learning Aprendizaje Profundo Sismicidad Colombiana Autopicado
dc.subject.lemb.spa.fl_str_mv	Protección y prevención ante los sismos Predicción sísmica
dc.subject.lemb.eng.fl_str_mv	Earthquakes - prevention and protection Earthquake prediction
dc.subject.proposal.eng.fl_str_mv	Autopicking PhaseNet EQTransformer Colombian seismicity Deep Learning
dc.subject.proposal.spa.fl_str_mv	Aprendizaje Profundo Sismicidad Colombiana Autopicado
description	ilustraciones, mapas + anexo
publishDate	2022
dc.date.issued.none.fl_str_mv	2022
dc.date.accessioned.none.fl_str_mv	2023-01-27T20:46:46Z
dc.date.available.none.fl_str_mv	2023-01-27T20:46:46Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/83173
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/83173 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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Earthquake fingerprints: Extracting waveform features for similarity-based earthquake detection. Pure and Applied Geophysics, 176. [Bernal Olaya and Vargas, 2015] Bernal Olaya, R. and Vargas, C. (2015). Earthquake, Tomographic, Seismic Reflection, and Gravity Evidence for a Shallowly Dipping Subduction Zone beneath the Caribbean Margin of Northwestern Colombia, pages 247–270. [Bormann and Saul, 2008] Bormann, P. and Saul, J. (2008). The New IASPEI Standard Broadband Magnitude mB. Seismological Research Letters, 79(5):698–705. [Chiarabba et al., 2016] Chiarabba, C., Gori, P. D., Faccenna, C., Speranza, F., Seccia, D., Dionicio, V., and Prieto, G. A. (2016). Subduction system and flat slab beneath the eastern cordillera of colombia. Geochemistry, 17:16 – 27. [Cichowicz, 1993] Cichowicz, A. (1993). An automatic s-phase picker. Bulletin of the Seismological Society of America, 83:180–189. [Cornthwaite et al., 2021] Cornthwaite, J., Bezada, M. J., Miao, W., Schmitz, M., Prieto, G. A., Dionicio, V., Niu, F., and Levander, A. (2021). Caribbean Slab Segmentation Beneath North-west South America Revealed by 3-D Finite Frequency Teleseismic P-Wave Tomography. Geo chemistry, Geophysics, Geosystems, 22(4):1–19. [Cornthwaite et al., 2017] Cornthwaite, J., Miao, W., Levander, A., Niu, F., Schmitz, M., Dionicio, V., Nader, M., and Bezada, M. (2017). Initial results from the carmarray seismic experiment in northern colombia and estern venezuel [Cortés et al., 2005] Cortés, M., Angelier, J., and Colletta, B. (2005). Paleostress evolution of the northern andes (eastern cordillera of colombia): Implications on plate kinematics of the south caribbean region. Tectonics, 24. [Dai and MacBeth, 1997] Dai, H. and MacBeth, C. (1997). The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings. Journal of Geophysical Research: Solid Earth, 102(B7):15105–15113 [Dokht et al., 2019] Dokht, R. M. H., Kao, H., Visser, R., and Smith, B. (2019). Seismic Event and Phase Detection Using Time–Frequency Representation and Convolutional Neural Networks. Seismological Research Letters, 90(2A):481–490 [Earle and Shearer, 1994] Earle, P. S. and Shearer, P. M. (1994). Characterization of global seismograms using an automatic-picking algorithm. Bulletin of the Seismological Society of America, 84(2):366–376. [Fuenzalida et al., 1998] Fuenzalida, H., Dimate, C., and Taboada, A. (1998). Sismotectónica de colombia: deformación continental activa y subducción. Física de la tierra, ISSN 0214-4557, Nº 10, 1998 (Ejemplar dedicado a: Sismicidad y sismotectónica de Centro y Sudamérica), pags. 111-148. [Gentili and Michelini, 2006] Gentili, S. and Michelini, A. (2006). Automatic picking of P and S phases using a neural tree. Journal of Seismology, 10(1):39–63. [Gibbons and Ringdal, 2006] Gibbons, S. J. and Ringdal, F. (2006). 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(2020). Weighted template-matching algorithm (wtma) for improved foreshock detection of the 2019 ridgecrest earthquake se quence. Bulletin of the Seismological Society of America, 110(4):1832–1844. [Liu and Zhang, 2014] Liu, H. and Zhang, J.-z. (2014). Sta/lta algorithm analysis and improvement of microseismic signal automatic detection. Progress in Geophysics, 29(4):1708–1714 [Lomax et al., 2000] Lomax, A., Virieux, J., Volant, P., and Berge-Thierry, C. (2000). Probabilistic Earthquake Location in 3D and Layered Models, pages 101–134. [Londoño et al., 2019] Londoño, J. M., Quintero, S., Vallejo, K., Muñoz, F., and Romero, J. (2019). Seismicity of valle medio del magdalena basin, colombia. Journal of South American Earth Sciences, 92:565–585. [Londoño et al., 2020] Londoño, J. M., Vallejo, K., and Quintero, S. (2020). Detailed seismic velocity structure of the caribbean and nazca plates beneath valle medio del magdalena region of ne colombia. Journal of South American Earth Sciences, 103:102762. [Lopez et al., 2020] Lopez, C. M., Velasquez, L., and Dionicio, V. (2020). Calibration of local magnitude scale for colombia. Bulletin of the Seismological Society of America, 110:1971–1981 [Magrini et al., 2020] Magrini, F., Jozinović, D., Cammarano, F., Michelini, A., and Boschi, L. (2020). Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale. Artificial Intelligence in Geosciences, 1:1–10. [Malavé and Suárez, 1995] Malavé, G. and Suárez, G. (1995). Intermediate-depth seismicity in northern colombia and western venezuela and its relationship to caribbean plate subduction. Tectonics, 14(3):617–628. [Marmureanu, 2009] Marmureanu, A. (2009). Rapid magnitude determination for vrancea early warning system. Romanian Journal of Physics, 54. [Martinez, 2016] Martinez, D. (2016). Nazcla plate geometry through seimological data. Undergraduate thesis, Universidad de Caldas, Manizales, Colombia. [Martínez and Prieto, 2020] Martínez, D. and Prieto, G. A. (2020). Implementación del método de relocalización source specific station terms para colombia: Aplicación para el occidente colombiano. Master’s thesis, Universidad Nacional de Colombia, Bogotá, Colombia. [Mayorga et al., 2020] Mayorga, E., Dionicio, V., Lizarazo, M., Pedraza, P., Poveda, E., Mercado, O., Siervo, D., Aguirre, L., Bolaños, R., Garzón, F., et al. (2020). El sismo de mesetas, meta del 24 de diciembre de 2019 aspectos sismológicos, movimiento fuerte y consideraciones geodésicas. Bogotá: Servicio Geológico Colombiano [McBrearty, 2021] McBrearty, I., . B. G. C. (2021). Earthquake Phase Association with Graph Neural Networks. [McBrearty et al., 2019a] McBrearty, I. W., Delorey, A. A., and Johnson, P. A. (2019a). Pairwise Association of Seismic Arrivals with Convolutional Neural Networks. Seismological Research Letters, 90(2A):503–509. [McBrearty et al., 2019b] McBrearty, I. W., Gomberg, J., Delorey, A. A., and Johnson, P. A. (2019b). Earthquake Arrival Association with Backprojection and Graph Theory. Bulletin of the Seismological Society of America, 109(6):2510–2531 [McEvilly and Majer, 1982] McEvilly, T. V. and Majer, E. L. (1982). ASP: An Automated Seismic Processor for microearthquake networks. Bulletin of the Seismological Society of America, 72(1):303–325. [Micallef, 2019] Micallef, T. (2019). Earthquake detection and localisation using the NARS-Botswana data. Master’s thesis, Uthrecht University, Utrecht, Netherlands. [Molina et al., 2020] Molina, I., Velasquez, J., Rubinstein, J., Garcia, A., and Dionicio, V. (2020). Seismicity induced by massive wastewater injection near puerto gaitán, colombia. Geophysical Journal International, 223:777–791. [Mousavi et al., 2020] Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., and Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications, 11(1):1–12. [Mousavi et al., 2019] Mousavi, S. M., Sheng, Y., Zhu, W., and Beroza, G. C. (2019). Stanford earthquake dataset (stead): A global data set of seismic signals for ai. IEEE Access, 7:179464–179476. [Ojeda and Havskov, 2001] Ojeda, A. and Havskov, J. (2001). Crustal structure and local seismicity in Colombia. Journal of Seismology, 5:575–593. [Pardo et al., 2019] Pardo, E., Garfias, C., and Malpica, N. (2019). Seismic phase picking using convolutional networks. IEEE Transactions on Geoscience and Remote Sensing, 57(9):7086–7092. [Pardo Trujillo, 2007] Pardo Trujillo, A., V. C. B. D. M. J. (2007). Colombian Sedimentary Basins: Nomenclature, Boundaries and Petroleum Geology. Agencia Nacional De Hidrocarburos-ANH and BM Exploration Ltd. Bogota, Colombia. [Potsdam, 2018] Potsdam, G. (2018). SeisComP3 documentation. [Prieto, 2022] Prieto, G. A. (2022). The Multitaper Spectrum Analysis Package in Python. Seismological Research Letters, 93(3):1922–1929. [Prieto et al., 2012] Prieto, G. A., Beroza, G. C., Barrett, S. A., López, G. A., and Florez, M. (2012). Earthquake nests as natural laboratories for the study of intermediate-depth earthquake mechanics. Tectonophysics, 570:42–56. [Richards-Dinger and Shearer, 2000] Richards-Dinger, K. and Shearer, P. (2000). Earthquake locations in southern california obtained using source-specific station terms. Journal of Geophys ical Research, 105:10939–10960. [Ringler et al., 2019] Ringler, A., Steim, J., Wilson, D., Widmer-Schnidrig, R., and Anthony, R. (2019). Improvements in seismic resolution and current limitations in the global seismographic network. Geophysical Journal International, 220. [Ronneberger et al., 2015] Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597. [Ross et al., 2018a] Ross, Z., Meier, M.-A., and Hauksson, E. (2018a). P wave arrival picking and first-motion polarity determination with deep learning. Journal of Geophysical Research: Solid Earth, 123 [Ross and Ben-Zion, 2014] Ross, Z. E. and Ben-Zion, Y. (2014). Automatic picking of direct P, S seismic phases and fault zone head waves. Geophysical Journal International, 199(1):368–381. [Ross et al., 2018b] Ross, Z. E., Meier, M., Hauksson, E., and Heaton, T. H. (2018b). Generalized Seismic Phase Detection with Deep Learning. Bulletin of the Seismological Society of America, 108(5A):2894–2901. [Ross et al., 2019a] Ross, Z. E., Trugman, D. T., Hauksson, E., and Shearer, P. M. (2019a). Searching for hidden earthquakes in southern california. Science, 364(6442):767–771. [Ross et al., 2019b] Ross, Z. E., Yue, Y., Meier, M.-A., Hauksson, E., and Heaton, T. H. (2019b). Phaselink: A deep learning approach to seismic phase association. Journal of Geophysical Research: Solid Earth, 124(1):856–869. [Sheen and Friberg, 2021] Sheen, D.-H. and Friberg, P. A. (2021). Seismic phase association based on the maximum likelihood method. Frontiers in Earth Science, 9. [Sleeman and van Eck, 1999] Sleeman, R. and van Eck, T. (1999). Robust automatic p-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Physics of the Earth and Planetary Interiors, 113(1):265–275. [Sun et al., 2022] Sun, M., Bezada, M. J., Cornthwaite, J., Prieto, G. A., Niu, F., and Levander, A. (2022). Overlapping slabs: Untangling subduction in nw south america through finite-frequency teleseismic tomography. Earth and Planetary Science Letters, 577:117253 [Suárez et al., 2008] Suárez, G., Eck, T., Giardini, D., Ahern, T., Butler, R., and Tsuboi, S. (2008). The international federation of digital seismograph networks (fdsn): An integrated system of seismological observatories. Systems Journal, IEEE, 2:431 – 438. [Syracuse et al., 2016] Syracuse, E., Maceira, M., Prieto, G., Zhang, H., and Ammon, C. (2016). Multiple plates subducting beneath colombia, as illuminated by seismicity and velocity from the joint inversion of seismic and gravity data. Earth and Planetary Science Letters, 444:139–149. [Taboada et al., 2000] Taboada, A., Rivera, L., Fuenzalida, H., Cisternas, A., Philip, H., Bijwaard, H., Olaya, J., and Rivera, C. (2000). Geodynamics of the northern andes: Subductions and intracontinental deformation (colombia). Tectonics, 19. [Trnkoczy, 2009] Trnkoczy, A. (2009). Understanding and parameter setting of sta/lta trigger algorithm. In New Manual of Seismological Observatory Practice (NMSOP), pages 1–20. Deutsches GeoForschungsZentrum GFZ. [Trugman and Shearer, 2017] Trugman, D. T. and Shearer, P. M. (2017). GrowClust: A Hierarchical Clustering Algorithm for Relative Earthquake Relocation, with Application to the Spanish Springs and Sheldon, Nevada, Earthquake Sequences. Seismological Research Letters, 88(2A):379–391 [van Benthem et al., 2013] van Benthem, S., Govers, R., Spakman, W., and Wortel, R. (2013). Tectonic evolution and mantle structure of the caribbean. Journal of Geophysical Research: Solid Earth, 118(6):3019–3036. [Vargas and Mann, 2013] Vargas, C. A. and Mann, P. (2013). Tearing and Breaking Off of Subducted Slabs as the Result of Collision of the Panama ArcffIndenter with Northwestern South America. Bulletin of the Seismological Society of America, 103(3):2025–2046. [Wagner et al., 2017] Wagner, L., Jaramillo, J., Ramirez-Hoyos, L., Monsalve, G., Cardona, A., and Becker, T. (2017). Transient slab flattening beneath colombia. Geophysical Research Letters, 44. [Waldhauser, 2001] Waldhauser, F. (2001). hypodd–a program to compute double-difference hypocenter locations. [Wang et al., 2019] Wang, J., Xiao, Z., Liu, C., Zhao, D., and Yao, Z. (2019). Deep learning for picking seismic arrival times. Journal of Geophysical Research: Solid Earth, 124(7):6612–6624. [White et al., 2020] White, M., Fang, H., Nakata, N., and Ben-Zion, Y. (2020). Pykonal: A python package for solving the eikonal equation in spherical and cartesian coordinates using the fast marching method. Seismological Research Letters, 91. [Wielandt et al., 2021] Wielandt, E., Caneva, A., and Vargas, C. A. (2021). Desarrollo de los instrumentos de detección y de registro de señales sísmicas. metadatos de las redes sismológicas de la región de latinoamérica y el caribe. RACCEFYN, 45(174):313–332. [Woollam et al., 2019] Woollam, J., Rietbrock, A., Bueno, A., and De Angelis, S. (2019). Convolutional Neural Network for Seismic Phase Classification, Performance Demonstration over a Local Seismic Network. Seismological Research Letters, 90(2A):491–502. [Yarce et al., 2014] Yarce, J., Monsalve, G., Becker, T., Cardona, A., Poveda, E., Alvira, D., and Ordóñez-Carmona, O. (2014). Seismological observations in northwestern south america: Evidence for two subduction segments, contrasting crustal thicknesses and upper mantle flow. Tectonophysics, 637. [Yeck et al., 2019] Yeck, W. L., Patton, J. M., Johnson, C. E., Kragness, D., Benz, H. M., Earle, P. S., Guy, M. R., and Ambruz, N. B. (2019). GLASS3: A standalone multiscale seismic detection associator. Bulletin of the Seismological Society of America, 109(4):1469–1478. [Zarifi et al., 2007] Zarifi, Z., Havskov, J., and Hanyga, A. (2007). An insight into the bucaramanga nest. Tectonophysics, 443:93–105. [Zhu and Beroza, 2019] Zhu, W. and Beroza, G. C. (2019). PhaseNet: A deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1):261–273. [Zhu et al., 2021] Zhu, W., McBrearty, I. W., Mousavi, S. M., Ellsworth, W. L., and Beroza, G. C. (2021). Earthquake Phase Association using a Bayesian Gaussian Mixture Model. pages 1–16.
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dc.rights.license.spa.fl_str_mv	Reconocimiento 4.0 Internacional
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dc.format.extent.spa.fl_str_mv	xii, 64 páginas + anexos
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dc.coverage.country.none.fl_str_mv	Colombia
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Geofísica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá - Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Prieto Gómez, Germán Andrésf4b1fdb24cfa6e27fe443f67c37eb290Castillo Taborda, Emmanuel Davidf54e11939e13b2951ffb5597dcf79971Servicio Geológico ColombianoLevander, AlanSiervo Plata, Daniel DavidCastillo, Emmanuel [0000-0002-9799-9775]Castillo, Emmanuel [0001730420]Castillo, Emmanuel [https://www.researchgate.net/profile/Emmanuel_Castillo4]Castillo, Emmanuel2023-01-27T20:46:46Z2023-01-27T20:46:46Z2022https://repositorio.unal.edu.co/handle/unal/83173Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, mapas + anexoLas redes sismológicas, ya sean mundiales, regionales o locales, tienen como objetivo vigilar la actividad sísmica. Esto implica la detección de eventos sísmicos y la determinación de su localización (latitud, longitud, profundidad y tiempo de origen) con un nivel aceptable de incertidumbre. Aplicamos estos pasos en tres redes sísmicas de forma automática. Una red sísmica regional (Red Sismológica Colombiana-CM, separación entre estaciones ~ 100 km), y dos redes locales y temporales (separación entre estaciones ~ 10-30 km) en el norte de Suramérica: Las redes sísmicas locales del Valle Medio de Magdalena (VMM) y de los Andes del Caribe-Mérida (YU). Para ello, es necesario procesar los datos continuos de múltiples estaciones para detectar y picar las fases sísmicas (normalmente ondas de cuerpo). En muchas redes, este proceso lo lleva a cabo un analista que, examinando visualmente las trazas, determina el tiempo de llegada de cada onda a una estación. Sin embargo, en redes sísmicas densas o en despliegues temporales, esta tarea puede ser muy laboriosa y requerir varios analistas. Para detectar y picar las fases sísmicas automáticamente de la red CM, utilizamos dos modelos de Deep Learning pre-entrenados: EQTransformer y PhaseNet. Derivamos algunas estadísticas para comparar el rendimiento tanto en fiabilidad como en compatibilidad con el algoritmo de asociación y localización Scanloc. Basándonos en lo anterior, utilizamos solo EQTransformer para las dos redes locales. El catálogo CM generado por los picks de PhaseNet y EQTransformer se comparó con el catálogo manual. Ambos catálogos son suficientemente confiables para mostrar una distribución similar de la sismicidad intermedia y somera del territorio colombiano. Las redes locales muestran un patrón más detallado de la localización de la sismicidad. Por último, fusionamos los catálogos en uno solo catálogo sísmico automático y usamos algunos cortes para identificar estructuras tectónicas regionales y resaltar fallas regionales. Los resultados muestran que esta implementación es lo suficientemente fiable como para generar catálogos sísmicos automáticos con la calidad adecuada en términos de errores de localización de eventos y es capaz de definir las principales estructuras tectónicas. Mejor aún, puede mejorar los tiempos de procesamiento de terremotos y complementar los catálogos manuales gracias a su buen rendimiento para terremotos pequeños y réplicas. (Texto tomado de la fuente)Seismological networks, whether global, regional, or local, have the objective of monitoring seismic activity. This implies the detection of seismic events and determination of their location (latitude, longitude, depth and origin time) with an acceptable level of uncertainty. We apply these steps in three seismic networks automatically. A regional seismic network (Colombian Seismological Network-CM, station separation ~ 100 km), and two local and temporary networks (station separation ~ 10-30 km) in northern South America: the Middle Magdalena Valley Array (VMM), and the Carribean-Mérida Andes seismic array (YU). To achieve this, continuous data of multiple stations needs to be processed to detect and pick seismic phases (usually body waves). In many networks this process is carried out by an analyst who, visually examining the traces, determines the arrival time of a wave at a station. However, for dense seismic networks or temporary deployments, this task can be very laborious, requiring several analysts. To detect and pick the seismic phases automatically of the CM network, we use two pre-trained Deep Learning models: EQTransformer and PhaseNet. We derive some statistics to compare the performance in both reliability and compatibility with the Scanloc association and location algorithm. Based on the above, we use only EQTransformer for the two local networks. The CM catalog generated by the PhaseNet and EQTransformer picks was compared with the manual catalog. Both catalogs are sufficiently reliable to show asimilar distribution of intermediate and shallow seismicity in the Colombian territory. The local networks show a more detailed patterns of seismicity locations. At last, we merge the catalogs in only one automatic seismic catalog and use some transects to identify regional tectonic structures and highlight regional faults. The results show that this implementation is reliable enough to generate automatic seismic catalogs with the appropriate quality in terms of the event location errors and is capable of defining major tectonic structures. Better yet, it can improve earthquake processing times and complement manual catalogs due to its good performance for small earthquakes and aftershocks.MaestríaMagíster en Ciencias - GeofísicaSismologíaSeismologyxii, 64 páginas + anexosapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - GeofísicaFacultad de CienciasBogotá - ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores550 - Ciencias de la tierra::558 - Ciencias de la tierra de América del SurProtección y prevención ante los sismosPredicción sísmicaEarthquakes - prevention and protectionEarthquake predictionAutopickingPhaseNetEQTransformerColombian seismicityDeep LearningAprendizaje ProfundoSismicidad ColombianaAutopicadoMonitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundoMonitoring seismic activity in the Colombian territory using Deep LearningTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMColombia[Al-Hashmi et al., 2013] Al-Hashmi, S., Rawlins, A., and Vernon, F. 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Earthquake Phase Association using a Bayesian Gaussian Mixture Model. pages 1–16.EstudiantesInvestigadoresMaestrosMedios de comunicaciónPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83173/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1037658661.2022.pdf1037658661.2022.pdfTesis de Maestría en Ciencias - Geofísicaapplication/pdf29413956https://repositorio.unal.edu.co/bitstream/unal/83173/2/1037658661.2022.pdfd384e98a4e8d5f53cc2ed26fb5d324caMD521037658661.2022.Exposicion.pdf1037658661.2022.Exposicion.pdfPresentación de la sustentación de Tesis de Maestría en Ciencias Geofísicaapplication/pdf7080707https://repositorio.unal.edu.co/bitstream/unal/83173/3/1037658661.2022.Exposicion.pdf652e782480c799819d6eec3d5bc0fca1MD53THUMBNAIL1037658661.2022.pdf.jpg1037658661.2022.pdf.jpgGenerated 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Monitoreo de la actividad sísmica del territorio colombiano usando aprendizaje profundo

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