Mostrar el registro sencillo del ítem
Proposal for a system model for offline seismic event detection in Colombia
| dc.contributor.author | Miranda, Julian | |
| dc.contributor.author | Flórez Abril, Angélica | |
| dc.contributor.author | Ospina, Gustavo | |
| dc.contributor.author | Gamboa, Ciro Alberto | |
| dc.contributor.author | Altuve, Miguel | |
| dc.contributor.author | FLOREZ-GONGORA, CARLOS | |
| dc.date.accessioned | 2021-11-06T18:05:47Z | |
| dc.date.available | 2021-11-06T18:05:47Z | |
| dc.date.issued | 2020-12-18 | |
| dc.identifier.uri | http://repositorio.ufps.edu.co/handle/ufps/710 | |
| dc.description.abstract | This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendations for its implementation. The resulting model allows understanding the integration of the phases required for the design and development of an offline seismic event detection system. | eng |
| dc.format.extent | 17 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | eng | spa |
| dc.publisher | Future Internet | spa |
| dc.relation.ispartof | Future Internet | |
| dc.rights | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | eng |
| dc.source | https://www.mdpi.com/1999-5903/12/12/231 | spa |
| dc.title | Proposal for a system model for offline seismic event detection in Colombia | eng |
| dc.type | Artículo de revista | spa |
| dcterms.references | El Tiempo, C.E.E. ¿Cuán Vulnerable es Colombia Ante un Sismo? Available online: https://www.eltiempo.c om/archivo/documento/CMS-16571309 (accessed on 5 November 2019). | spa |
| dcterms.references | El Tiempo, C.E.E. Los Peores Terremotos en la Historia de Colombia. Available online: https://www.eltiempo .com/colombia/otras-ciudades/terremotos-mas-fuertes-de-colombia-155006 (accessed on 18 February 2020). | spa |
| dcterms.references | Wen-xiang, J.; Hai-ying, Y.; Li, L. A Robust Algorithm for Earthquake Detector. Available online: /paper/A-Robust-Algorithm-for-Earthquake-Detector-Wen-xiang-Hai-ying/fa34661c1689cc9ee9a871ae4e1 740bf323a54d2 (accessed on 18 February 2020). | spa |
| dcterms.references | Cuéllar, A.; Suárez, G.; Espinosa-Aranda, J.M. Performance Evaluation of the Earthquake Detection and Classification Algorithm 2( tS–tP) of the Seismic Alert System of Mexico (SASMEX). Bull. Seismol. Soc. Am. 2017, 107, 1451–1463. [CrossRef] | spa |
| dcterms.references | Sharma, B.K.; Kumar, A.; Murthy, V.M. Evaluation of seismic events detection algorithms. J. Geol. Soc. India 2010, 75, 533–538. [CrossRef] | spa |
| dcterms.references | USGS 20 Largest Earthquakes in the World. Available online: https://www.usgs.gov/natural-hazards/earthqu ake-hazards/science/20-largest-earthquakes-world?qt-science_center_objects=0#qt-science_center_objects (accessed on 27 November 2020). | spa |
| dcterms.references | Sarria Molina, A. Ingeniería Sísmica; Ediciones Uniandes, Ecoe Ediciones: Bogotá, Colombia, 1995; ISBN 958-9057-49-7. | spa |
| dcterms.references | Frohlich, C.; Kadinsky-Cade, K.; Davis, S.D. A reexamination of the Bucaramanga, Colombia, earthquake nest. Bull. Seismol. Soc. Am. 1995, 85, 1622–1634. | spa |
| dcterms.references | Prieto, G.A.; Beroza, G.C.; Barrett, S.A.; López, G.A.; Florez, M. Earthquake nests as natural laboratories for the study of intermediate-depth earthquake mechanics. Tectonophysics 2012, 570, 42–56. | spa |
| dcterms.references | Bernal-Olaya, R.; Mann, P.; Vargas, C.A. Earthquake, tomographic, seismic reflection, and gravity evidence for a shallowly dipping subduction zone beneath the Caribbean Margin of Northwestern Colombia. In Petroleum Geology and Potential of the Colombian Caribbean Margin; American Association of Petroleum Geologists: Tulsa, OK, USA, 2015; pp. 247–269. | spa |
| dcterms.references | Ochoa, L.H.; Niño, L.F.; Vargas, C.A. Fast magnitude determination using a single seismological station record implementing machine learning techniques. Geod. Geodyn. 2018, 9, 34–41. [ | spa |
| dcterms.references | Konovalov, A.V.; Stepnov, A.A.; Patrikeev, V.N. SEISAN software application for developing an automated seismological data analysis workstation. Seism. Instrum. 2012, 48, 270–281. | spa |
| dcterms.references | Stepnov, A.A.; Gavrilov, A.V.; Konovalov, A.V.; Ottemöller, L. New architecture of an automated system for acquisition, storage, and processing of seismic data. Seism. Instrum. 2014, 50, 67–74. | spa |
| dcterms.references | Havskov, J.; Ottemoller, L. SEISAN earthquake analysis software. Seismol. Res. Lett. 1999, 70, 532–534. | spa |
| dcterms.references | Weber, B.; Becker, J.; Hanka, W.; Heinloo, A.; Hoffmann, M.; Kraft, T.; Pahlke, D.; Reinhardt, J.; Thoms, H. SeisComP3—Automatic and interactive real time data processing. Geophys. Res. Abstracts 2007, 9, 09219. | spa |
| dcterms.references | Alfaro, A. Difficulties on psha in Colombia because of data scarcity. Bull. Int. Inst. Seismol. Earthq. Eng. 2012, 46, 145–152. | spa |
| dcterms.references | Mora-Páez, H.; Peláez-Gaviria, J.-R.; Diederix, H.; Bohórquez-Orozco, O.; Cardona-Piedrahita, L.; Corchuelo-Cuervo, Y.; Ramírez-Cadena, J.; Díaz-Mila, F. Space Geodesy Infrastructure in Colombia for Geodynamics Research. Seismol. Res. Lett. 2018, 89, 446–451. | spa |
| dcterms.references | Zhu, W.; Mousavi, S.M.; Beroza, G.C. Seismic Signal Denoising and Decomposition Using Deep Neural Networks. IEEE Trans. Geosci. Remote Sens. 2019, 57, 9476–9488. | spa |
| dcterms.references | Perol, T.; Gharbi, M.; Denolle, M. Convolutional neural network for earthquake detection and location. Sci. Adv. 2018, 4, e1700578. | spa |
| dcterms.references | Hildyard, M.W.; Nippress, S.E.J.; Rietbrock, A. Event Detection and Phase Picking Using a Time-Domain Estimate of Predominate Period Tpd. Bull. Seismol. Soc. Am. 2008, 98, 3025–3032. | spa |
| dcterms.references | Toledo Peña, P.A. Algoritmo de Detección de Ondas P Invariante de Escala: Caso de Réplicas del Sismo del 11 de Marzo de 2010. Ph.D. Thesis, Universidad de Chile, Santiago, Chile, 2014. | spa |
| dcterms.references | Allen, R.M.; Gasparini, P.; Kamigaichi, O.; Bose, M. The status of earthquake early warning around the world: An introductory overview. Seismol. Res. Lett. 2009, 80, 682–693. | spa |
| dcterms.references | Joswig, M. Pattern recognition for earthquake detection. Bull. Seismol. Soc. Am. 1990, 80, 170–186. | spa |
| dcterms.references | Donoho, D.L. Nonlinear Wavelet Methods for Recovery of Signals, Densities, and Spectra from Indirect and Noisy Data. 1993. Available online: https://statistics.stanford.edu/sites/g/files/sbiybj6031/f/EFS%20NSF%20 437.pdf (accessed on 2 December 2020). | spa |
| dcterms.references | Hafez, A.G.; Khan, T.A.; Kohda, T. Earthquake onset detection using spectro-ratio on multi-threshold time–frequency sub-band. Digit. Signal Process. 2009, 19, 118–126. | spa |
| dcterms.references | Withers, M.; Aster, R.; Young, C.; Beiriger, J.; Harris, M.; Moore, S.; Trujillo, J. A comparison of select trigger algorithms for automated global seismic phase and event detection. Bull. Seismol. Soc. Am. 1998, 88, 95–106. | spa |
| dcterms.references | Allen, R.V. Automatic earthquake recognition and timing from single traces. Bull. Seismol. Soc. Am. 1978, 68, 1521–1532. | spa |
| dcterms.references | Leonard, M. Comparison of Manual and Automatic Onset Time Picking. Bull. Seismol. Soc. Am. 2000, 90, 1384–1390. | spa |
| dcterms.references | Li, Z.; Peng, Z.; Hollis, D.; Zhu, L.; McClellan, J. High-resolution seismic event detection using local similarity for Large-N arrays. Sci. Rep. 2018, 8, 1646. | spa |
| dcterms.references | Schorlemmer, D.; Woessner, J. Probability of Detecting an Earthquake. Bull. Seismol. Soc. Am. 2008, 98, 2103–2117. | spa |
| dcterms.references | Yoon, C.E.; O’Reilly, O.; Bergen, K.J.; Beroza, G.C. Earthquake detection through computationally efficient similarity search. Sci. Adv. 2015, 1, e1501057. | spa |
| dcterms.references | Avvenuti, M.; Cresci, S.; Marchetti, A.; Meletti, C.; Tesconi, M. EARS (Earthquake Alert and Report System): A Real Time Decision Support System for Earthquake Crisis Management. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 24–27 August 2014; ACM: New York, NY, USA, 2014; pp. 1749–1758. | spa |
| dcterms.references | Sakaki, T.; Okazaki, M.; Matsuo, Y. Earthquake shakes Twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA, 26–30 April 2010; ACM: New York, NY, USA, 2010; pp. 851–860. | spa |
| dcterms.references | Earle, P.S.; Bowden, D.C.; Guy, M. Twitter earthquake detection: Earthquake monitoring in a social world. Ann. Geophys. 2012, 54. | spa |
| dcterms.references | Liu, H.; Zhang, J.Z. STA/LTA algorithm analysis and improvement of Microseismic signal automatic detection. Prog. Geophys. 2014, 29, 1708–1714. | spa |
| dcterms.references | Havskov, J.; Ottemöller, L. Seisan: The Earthquake Analysis Software for Windows, Solaris, Linuz and MacOSx Version 8.1; Institute of Solid Earth Science, University of Bergen: Bergen, Norway, 2008. | spa |
| dcterms.references | IRIS: Data Formats. Available online: https://ds.iris.edu/ds/nodes/dmc/data/formats/ (accessed on 19 February 2020). | spa |
| dcterms.references | Pavlis, G. SEISPP Library: C++ Seismic Data Processing Library. Available online: https://pavlab.sitehost.iu .edu/software/seispp/html/index.html (accessed on 9 October 2020). | spa |
| dcterms.references | Beyreuther, M.; Barsch, R.; Krischer, L.; Megies, T.; Behr, Y.; Wassermann, J. ObsPy: A Python Toolbox for Seismology. Seismol. Res. Lett. 2010, 81, 530–533. | spa |
| dcterms.references | United States Geological Survey Earthquake Hazards—Software. Available online: https://www.usgs.gov/n atural-hazards/earthquake-hazards/software (accessed on 19 February 2020). | spa |
| dcterms.references | Miranda Calle, J.D.; Gamboa Entralgo, C.A. Desarrollo de un Sistema para la Detección de Movimientos Sísmicos usando Redes Neuronales Artificiales; Universidad Pontificia Bolivariana: Medellín, Colombia, 2018. | spa |
| dcterms.references | Chen, Z.; Stewart, R.R. A multi-window algorithm for real-time automatic detection and picking of p-phases of microseismic events. CREWES Res. Rep. 2006, 18. Available online: https://www.crewes.org/ForOurSpons ors/ResearchReports/2006/2006-15.pdf (accessed on 2 December 2020). | spa |
| dcterms.references | SeisComP3 Scautopick. Makes Picks on Waveforms. Available online: https://www.seiscomp3.org/doc/jaka rta/current/apps/scautopick.html (accessed on 19 February 2020). | spa |
| dcterms.references | Servicio Geológico Colombiano Consulta Catálogo Sísmico. Available online: https://www2.sgc.gov.co/sgc/ sismos/Paginas/catalogo-sismico.aspx (accessed on 11 February 2020). | spa |
| dcterms.references | Catálogo Mecanismo Focal y Tensor Momento. Available online: http://bdrsnc.sgc.gov.co/sismologia1/sism ologia/focal_seiscomp_3/index.html (accessed on 9 October 2020). | spa |
| dcterms.references | Zhang, J.; Tang, Y.; Li, H. STA/LTA Fractal Dimension Algorithm of Detecting the P-Wave ArrivalSTA/LTA Fractal Dimension Algorithm of Detecting the P-Wave Arrival. Bull. Seismol. Soc. Am. 2018, 108, 230–237. | spa |
| dcterms.references | Akram, J.; Peter, D.; Eaton, D. A k-mean characteristic function to improve STA/LTA detection. In Proceedings of the Geoconvention, Calgary, AB, Canada, 7–11 May 2018; p. 5. Available online: https://geoconvention.com/wp-content/uploads/abstracts/2018/287_GC2018_A_k-mean_characteri stic_function_to_improve_STALTA1_detection.pdf (accessed on 2 December 2020). | spa |
| dcterms.references | Gentili, S.; Michelini, A. Automatic picking of P and S phases using a neural tree. J. Seismol. 2006, 10, 39–63. | spa |
| dcterms.references | Audretsch, J.M.L. Earthquake Detection using Deep Learning Based Approaches. Master’s Thesis, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2020. | spa |
| dcterms.references | Wiszniowski, J.; Plesiewicz, B.M.; Trojanowski, J. Application of real time recurrent neural network for detection of small natural earthquakes in Poland. Acta Geophys. 2014, 62, 469–485. | spa |
| dcterms.references | Kong, Q.; Trugman, D.T.; Ross, Z.E.; Bianco, M.J.; Meade, B.J.; Gerstoft, P. Machine Learning in Seismology: Turning Data into Insights. Seismol. Res. Lett. 2019, 90, 3–14. | spa |
| dcterms.references | Venkatesh, B.; Anuradha, J. A Review of Feature Selection and Its Methods. Cybern. Inf. Technol. 2019, 19, 3–26. | spa |
| dcterms.references | Miranda, J.D.; Gamboa, C.A.; Flórez, A.; Altuve, M. Voting-based seismic data classification system using logistic regression models. In Proceedings of the 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), Bucaramanga, Colombia, 24–26 April 2019; pp. 1–5. | spa |
| dcterms.references | Bormann, P.; Wielandt, E. Seismic Signals and Noise. New Man. Seismol. Obs. Pract. 2 NMSOP2 2013, 1–62. | spa |
| dcterms.references | Kaur, K.; Wadhwa, M.; Park, E.K. Detection and identification of seismic P-Waves using Artificial Neural Networks. In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA, 4–9 August 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 1–6. | spa |
| dcterms.references | Beyreuther, M.; Hammer, C.; Wassermann, J.; Ohrnberger, M.; Megies, T. Constructing a Hidden Markov Model based earthquake detector: Application to induced seismicity: Constructing a HMM based earthquake detector. Geophys. J. Int. 2012, 189, 602–610. | spa |
| dcterms.references | Riggelsen, C.; Ohrnberger, M. A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations. Pure Appl. Geophys. 2014, 171, 395–411. | spa |
| dcterms.references | Ruano, A.E.; Madureira, G.; Barros, O.; Khosravani, H.R.; Ruano, M.G.; Ferreira, P.M. Seismic detection using support vector machines. Neurocomputing 2014, 135, 273–283. | spa |
| dcterms.references | Asencio–Cortés, G.; Morales–Esteban, A.; Shang, X.; Martínez–Álvarez, F. Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput. Geosci. 2018, 115, 198–210. | spa |
| dcterms.references | Li, W.; Narvekar, N.; Nakshatra, N.; Raut, N.; Sirkeci, B.; Gao, J. Seismic Data Classification Using Machine Learning. In Proceedings of the 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), Bamberg, Germany, 26–29 March 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 56–63. | spa |
| dcterms.references | Amidan, B.G.; Hagedom, D.N. Logistic Regression Applied to Seismic Discrimination; No. PNNL-12031; Pacific Northwest National Lab.: Richland, WA, USA, 1998. | spa |
| dcterms.references | Reynen, A.; Audet, P. Supervised machine learning on a network scale: Application to seismic event classification and detection. Geophys. J. Int. 2017, 210, 1394–1409. | spa |
| dcterms.references | Ibs-von Seht, M. Detection and identification of seismic signals recorded at Krakatau volcano (Indonesia) using artificial neural networks. J. Volcanol. Geotherm. Res. 2008, 176, 448–456. | spa |
| dcterms.references | HassanAitLaasri, E.; Akhouayri, E.-S.; Agliz, D.; Atmani, A. Seismic Signal Classification using Multi-Layer Perceptron Neural Network. Int. J. Comput. Appl. 2013, 79, 35–43. | spa |
| dcterms.references | Giudicepietro, F.; Esposito, A.M.; Ricciolino, P. Fast Discrimination of Local Earthquakes Using a Neural Approach. Seismol. Res. Lett. 2017, 88, 1089–1096. | spa |
| dcterms.references | Vallejos, J.A.; McKinnon, S.D. Logistic regression and neural network classification of seismic records. Int. J. Rock Mech. Min. Sci. 2013, 62, 86–95. | spa |
| dcterms.references | Diersen, S.; Lee, E.-J.; Spears, D.; Chen, P.; Wang, L. Classification of Seismic Windows Using Artificial Neural Networks. Procedia Comput. Sci. 2011, 4, 1572–1581. | spa |
| dcterms.references | Amei, A.; Fu, W.; Ho, C.-H. Time Series Analysis for Predicting the Occurrences of Large Scale Earthquakes. Int. J. Appl. Sci. Technol. 2012, 2, 12. | spa |
| dcterms.references | Adhikari, R.; Agrawal, R.K. An Introductory Study on Time series Modeling and Forecasting. arXiv 2013, arXiv:1302.6613. Available online: https://arxiv.org/abs/1302.6613 (accessed on 2 December 2020). | spa |
| dcterms.references | Yu, J.; Wang, R.; Liu, T.; Zhang, Z.; Wu, J.; Jiang, Y.; Sun, L.; Xia, P. Seismic energy dispersion compensation by multi-scale morphology. Pet. Sci. 2014, 11, 376–384. | spa |
| dcterms.references | Egrioglu, E.; Khashei, M.; Aladag, C.H.; Turksen, I.B.; Yolcu, U. Advanced Time Series Forecasting Methods. Available online: https://www.hindawi.com/journals/mpe/2015/918045/ (accessed on 9 October 2020). | spa |
| dcterms.references | Fong, S.; Deb, S.; Fong, S.; Deb, S. Prediction of Major Earthquakes as Rare Events Using RF-Typed Polynomial Neural Networks. In Encyclopedia of Information Science and Technology; IGI Global: Hershey, PN, USA, 2015; ISBN 978-1-4666-5888-2. | spa |
| dc.coverage.country | Colombia | |
| dc.identifier.doi | https://doi.org/10.3390/fi12120231 | |
| dc.publisher.place | Suiza | spa |
| dc.relation.citationedition | Vol.12 No.12.(2020) | spa |
| dc.relation.citationendpage | 17 | spa |
| dc.relation.citationissue | 12(2020) | spa |
| dc.relation.citationstartpage | 1 | spa |
| dc.relation.citationvolume | 12 | spa |
| dc.relation.cites | Miranda, J., Flórez, A., Ospina, G., Gamboa, C., Flórez, C., & Altuve, M. (2020). Proposal for a System Model for Offline Seismic Event Detection in Colombia. Future Internet, 12(12), 231. | |
| dc.relation.ispartofjournal | Future Internet | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.creativecommons | Atribución 4.0 Internacional (CC BY 4.0) | spa |
| dc.subject.proposal | seismic event detection | eng |
| dc.subject.proposal | detection model | eng |
| dc.subject.proposal | seismology | eng |
| dc.subject.proposal | classification | eng |
| dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/article | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
| dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
