Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación
La investigación hace uso del sistema de alerta E3WS para estimar sismos en el cluster sísmico de Murindo y Cauca.
- Autores:
-
Montenegro Folleco, Juan Andrés
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/68094
- Acceso en línea:
- http://hdl.handle.net/1992/68094
- Palabra clave:
- Murindo
Cauca
Sistema de alerta temprana
Machine learning
Geociencias
- Rights
- openAccess
- License
- Attribution-NoDerivatives 4.0 Internacional
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Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación |
| title |
Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación |
| spellingShingle |
Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación Murindo Cauca Sistema de alerta temprana Machine learning Geociencias |
| title_short |
Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación |
| title_full |
Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación |
| title_fullStr |
Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación |
| title_full_unstemmed |
Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación |
| title_sort |
Estimación de características de un sismo por medio de técnicas de aprendizaje automático a partir de una sola estación |
| dc.creator.fl_str_mv |
Montenegro Folleco, Juan Andrés |
| dc.contributor.advisor.none.fl_str_mv |
Nitescu, Bogdan |
| dc.contributor.author.none.fl_str_mv |
Montenegro Folleco, Juan Andrés |
| dc.contributor.jury.none.fl_str_mv |
Poveda, Esteban |
| dc.subject.keyword.none.fl_str_mv |
Murindo Cauca Sistema de alerta temprana Machine learning |
| topic |
Murindo Cauca Sistema de alerta temprana Machine learning Geociencias |
| dc.subject.themes.es_CO.fl_str_mv |
Geociencias |
| description |
La investigación hace uso del sistema de alerta E3WS para estimar sismos en el cluster sísmico de Murindo y Cauca. |
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2023 |
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2023-06-02 |
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Trabajo de grado - Pregrado |
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Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives. IEEE Signal Processing Magazine, 35(2), 20-30. https://doi.org/10.1109/msp.2017.2779166 Mo, H. Y., Sun, H., Liu, J., & Wei, S. (2019). Developing window behavior models for residential buildings using XGBoost algorithm. Energy and Buildings, 205, 109564. https://doi.org/10.1016/j.enbuild.2019.109564 Boada, M. A., Poveda, E., & Tary, J. B. (2022). Lithospheric and Slab Configurations From Receiver Function Imaging in Northwestern South America, Colombia. Journal Of Geophysical Research: Solid Earth, 127(12). https://doi.org/10.1029/2022jb024475 Mousavi, S. M., & Beroza, G. C. (2020). A Machine Learning Approach for Earthquake Magnitude Estimation. Geophysical Research Letters, 47(1). https://doi.org/10.1029/2019gl085976 Mousavi, S. M., & Beroza, G. C. (2020b). Bayesian-Deep-Learning Estimation of Earthquake Location From Single-Station Observations. IEEE Transactions on Geoscience and Remote Sensing, 58(11), 8211-8224. https://doi.org/10.1109/tgrs.2020.2988770 Mousavi, S. M., Sheng, Y. P., Zhu, W., & Beroza, G. C. (2019). STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI. IEEE Access, 7, 179464-179476. https://doi.org/10.1109/access.2019.2947848 Nuñez, Alejandra. (2016). Análisis del desempeño de la Red Sísmica del Noroeste de México para la evaluación y el control de calidad de los datos generados. http://dx.doi.org/10.13140/RG.2.2.13969.63847 Massachusetts Institute of Technology Department of Mechanical Engineering. (s.f). Understanding poles and zeros Pan, B. (2018). Application of XGBoost algorithm in hourly PM2.5 concentration prediction. IOP Conference Series: Earth and Environmental Science, 113, 012127. https://doi.org/10.1088/1755-1315/113/1/012127 Parhi, K. K., & Ayinala, M. (2014). Low-Complexity Welch Power Spectral Density Computation. IEEE Transactions on Circuits and Systems I-regular Papers, 61(1), 172-182. https://doi.org/10.1109/tcsi.2013.2264711 Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681-686. https://doi.org/10.1198/016214508000000337 Garcia, P. J., Vargas, C., & J, H. M. (2007). GEOMETRIC MODEL OF THE NAZCA PLATE SUBDUCTION IN SOUTHWEST COLOMBIA. Earth Sciences Research Journal, 11(2), 124-134. http://www.scielo.org.co/pdf/esrj/v11n2/v11n2a03.pdf Pennington, W. D. (1981). Subduction of the Eastern Panama Basin and seismotectonics of northwestern South America. Journal of Geophysical Research, 86(B11), 10753-10770. https://doi.org/10.1029/jb086ib11p10753 Perol, T., Gharbi, M., & Denolle, M. A. (2018). Convolutional neural network for earthquake detection and location. Science Advances, 4(2). https://doi.org/10.1126/sciadv.1700578 Poveda, S. (2022). REANALISIS SISMOTECTONICO DEL CLUSTER SISMICO DE MURINDO. Universidad de los Andes Prabhu, K. (2013). Window Functions and Their Applications in Signal Processing. Taylor and Francis Group. https://doi.org/10.1201/9781315216386 Ranstam, J., & Cook, J. (2018). LASSO regression. British Journal of Surgery, 105(10), 1348. https://doi.org/10.1002/bjs.10895 Scherbaum, F. (2001). RC Filter. En Modern approaches in geophysics (pp. 12-38). Springer Nature (Netherlands). https://doi.org/10.1007/978-1-4020-6861-4_2 Sella, G. F., Dixon, T. H., & Mao, A. (2002). REVEL: A model for Recent plate velocities from space geodesy. Journal of Geophysical Research, 107(B4), ETG 11-30. https://doi.org/10.1029/2000jb000033 Shearer, P. M. (2009). Introduction to seismology. Choice Reviews Online, 37(08), 37-4521. https://doi.org/10.5860/choice.37-4521 Houston, H. (2003). An Introduction to Seismology, Earthquakes, and Earth Structure An Introduction to Seismology, Earthquakes, and Earth Structure. Blackwell publishing. https://doi.org/10.1063/1.1629009 Suárez, G. (2022). The Seismic Early Warning System of Mexico (SASMEX): A Retrospective View and Future Challenges. Frontiers in Earth Science, 10. https://doi.org/10.3389/feart.2022.827236 Tary, J., Boada, M. A., Vargas, C., Monoga, A. M. M., Naranjo-Hernandez, D. F., & Quiroga, D. E. (2022). Source characteristics of the Mw 6 Mutatá earthquake, Murindo seismic cluster, northwestern Colombia. Journal of South American Earth Sciences, 115, 103728. https://doi.org/10.1016/j.jsames.2022.103728 Taylor, R. L. (1990). Interpretation of the Correlation Coefficient: A Basic Review. Journal of Diagnostic Medical Sonography, 6(1), 35-39. https://doi.org/10.1177/875647939000600106 Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the royal statistical society series b-methodological, 58(1), 267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x Ulrich, T. (2006). Envelope Calculation from the Hilbert Transform. Vargas, C. (2019). Subduction geometries in northwestern South America. doi: 10.32685/pub.esp.38.2019.11. Vargas, C., Pujades, L., & Montes, L. A. O. (2007). Seismic structure of South-Central Andes of Colombia by tomographic inversion. Geofisica Internacional, 46(2), 117-127. https://doi.org/10.22201/igeof.00167169p.2007.46.2.21 Vargas, C., & Mann, P. (2013). Tearing and Breaking Off of Subducted Slabs as the Result of Collision of the Panama Arc-Indenter with Northwestern South America. Bulletin of the Seismological Society of America, 103(3), 2025-2046. https://doi.org/10.1785/0120120328 Veloza, G., Styron, R. H., Taylor, M. D., & Mora, A. (2012). Open-source archive of active faults for northwest South America. GSA today, 22(10), 4-10. https://doi.org/10.1130/gsat-g156a.1 Wei, F., & Li, M. (2003). Cepstrum analysis of seismic source characteristics. Acta Seismologica Sinica, 16(1), 50-58. https://doi.org/10.1007/s11589-003-0006-9 Wielandt, E. (2012). Seismic Sensors and their Calibration. Streckeisen Seismic Instrumentation, 1-51. https://doi.org/10.2312/gfz.nmsop-2_ch5 Wu, C. (s. f.). hypo71 Tutorial. http://geophysics.eas.gatech.edu/people/cwu/teaching/hypo71/hypo71.html Yu, S., & Ma, J. (2021). Deep Learning for Geophysics: Current and Future Trends. Reviews of Geophysics, 59(3). https://doi.org/10.1029/2021rg000742 Zarifi, Z., Havskov, J., & Hanyga, A. (2007). An insight into the Bucaramanga nest. Tectonophysics, 443(1-2), 93-105. https://doi.org/10.1016/j.tecto.2007.06.004 Zhao, P., & Yu, B. (2006). On Model Selection Consistency of Lasso. Journal of Machine Learning Research, 7(90), 2541-2563. https://statistics.berkeley.edu/sites/default/files/tech-reports/702.pdf Zhou, Z. (2022). Machine Learning. Springer. |
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Attribution-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Nitescu, Bogdan02be7cac-ea72-4cf0-8de6-5f25edeea76f600Montenegro Folleco, Juan Andrés624c7d15-1aac-4d9d-a3ea-27d1878aec3c600Poveda, Esteban2023-07-04T21:32:11Z2023-07-04T21:32:11Z2023-06-02http://hdl.handle.net/1992/68094instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/La investigación hace uso del sistema de alerta E3WS para estimar sismos en el cluster sísmico de Murindo y Cauca.El 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. 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