Proposal for a system model for offline seismic event detection in Colombia
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 proce...
- Autores:
-
Miranda, Julian
Flórez Abril, Angélica
Ospina, Gustavo
Gamboa, Ciro Alberto
Altuve, Miguel
FLOREZ-GONGORA, CARLOS
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/710
- Palabra clave:
- seismic event detection
detection model
seismology
classification
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Proposal for a system model for offline seismic event detection in Colombia |
| title |
Proposal for a system model for offline seismic event detection in Colombia |
| spellingShingle |
Proposal for a system model for offline seismic event detection in Colombia seismic event detection detection model seismology classification |
| title_short |
Proposal for a system model for offline seismic event detection in Colombia |
| title_full |
Proposal for a system model for offline seismic event detection in Colombia |
| title_fullStr |
Proposal for a system model for offline seismic event detection in Colombia |
| title_full_unstemmed |
Proposal for a system model for offline seismic event detection in Colombia |
| title_sort |
Proposal for a system model for offline seismic event detection in Colombia |
| dc.creator.fl_str_mv |
Miranda, Julian Flórez Abril, Angélica Ospina, Gustavo Gamboa, Ciro Alberto Altuve, Miguel FLOREZ-GONGORA, CARLOS |
| dc.contributor.author.none.fl_str_mv |
Miranda, Julian Flórez Abril, Angélica Ospina, Gustavo Gamboa, Ciro Alberto Altuve, Miguel FLOREZ-GONGORA, CARLOS |
| dc.subject.proposal.eng.fl_str_mv |
seismic event detection detection model seismology classification |
| topic |
seismic event detection detection model seismology classification |
| description |
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. |
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2020 |
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2020-12-18 |
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2021-11-06T18:05:47Z |
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2021-11-06T18:05:47Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://repositorio.ufps.edu.co/handle/ufps/710 |
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https://doi.org/10.3390/fi12120231 |
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http://repositorio.ufps.edu.co/handle/ufps/710 https://doi.org/10.3390/fi12120231 |
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eng |
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eng |
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Future Internet |
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Vol.12 No.12.(2020) |
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17 |
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12(2020) |
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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. |
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Future Internet |
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Atribución 4.0 Internacional (CC BY 4.0) |
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Atribución 4.0 Internacional (CC BY 4.0) http://purl.org/coar/access_right/c_abf2 |
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17 páginas |
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application/pdf |
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Colombia |
| dc.publisher.spa.fl_str_mv |
Future Internet |
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Suiza |
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Miranda, Julianfbd027288a840d0ee6a78bcc87dda7e6600Flórez Abril, Angélicafdc7fe0a7c41f5a22771e305ad670f5e600Ospina, Gustavo14877194e51647f25fb96c4b2f2abd5e600Gamboa, Ciro Alberto3cb2624fd2a96aa5fe8d967336c57645600Altuve, Miguelbec97ceb960406561b949603270d2144600FLOREZ-GONGORA, CARLOSd7e3da47e061d57c74178b0bde75fa206002021-11-06T18:05:47Z2021-11-06T18:05:47Z2020-12-18http://repositorio.ufps.edu.co/handle/ufps/710https://doi.org/10.3390/fi12120231This 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.17 páginasapplication/pdfengFuture InternetSuizaFuture InternetVol.12 No.12.(2020)1712(2020)112Miranda, 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.Future Internet© 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/).info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://www.mdpi.com/1999-5903/12/12/231Proposal for a system model for offline seismic event detection in ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Colombiaseismic event detectiondetection modelseismologyclassificationEl 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).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).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).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]Sharma, B.K.; Kumar, A.; Murthy, V.M. Evaluation of seismic events detection algorithms. J. Geol. Soc. India 2010, 75, 533–538. [CrossRef]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).Sarria Molina, A. Ingeniería Sísmica; Ediciones Uniandes, Ecoe Ediciones: Bogotá, Colombia, 1995; ISBN 958-9057-49-7.Frohlich, C.; Kadinsky-Cade, K.; Davis, S.D. A reexamination of the Bucaramanga, Colombia, earthquake nest. Bull. Seismol. Soc. Am. 1995, 85, 1622–1634.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.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.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. [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.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.Havskov, J.; Ottemoller, L. SEISAN earthquake analysis software. Seismol. Res. Lett. 1999, 70, 532–534.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. 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A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations. Pure Appl. Geophys. 2014, 171, 395–411.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.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.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.Amidan, B.G.; Hagedom, D.N. Logistic Regression Applied to Seismic Discrimination; No. PNNL-12031; Pacific Northwest National Lab.: Richland, WA, USA, 1998.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.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.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.Giudicepietro, F.; Esposito, A.M.; Ricciolino, P. Fast Discrimination of Local Earthquakes Using a Neural Approach. Seismol. Res. Lett. 2017, 88, 1089–1096.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.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.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.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).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.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).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.TEXTProposal for a System Model for Offline Seismic Event Detection in Colombia.pdf.txtProposal for a System Model for Offline Seismic Event Detection in Colombia.pdf.txtExtracted texttext/plain87638https://repositorio.ufps.edu.co/bitstream/ufps/710/3/Proposal%20for%20a%20System%20Model%20for%20Offline%20Seismic%20Event%20Detection%20in%20Colombia.pdf.txtb281425280c75d412cbc19d6abd04835MD53open accessTHUMBNAILProposal for a System Model for Offline Seismic Event Detection in Colombia.pdf.jpgProposal for a System Model for Offline Seismic Event Detection in Colombia.pdf.jpgGenerated Thumbnailimage/jpeg14458https://repositorio.ufps.edu.co/bitstream/ufps/710/4/Proposal%20for%20a%20System%20Model%20for%20Offline%20Seismic%20Event%20Detection%20in%20Colombia.pdf.jpg624b32129ffdc7fac847721ddd73f308MD54open accessORIGINALProposal for a System Model for Offline Seismic Event 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
0000-0002-7580-2361fbd027288a840d0ee6a78bcc87dda7e6600 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