25 Years of Self-organized Criticality: Numerical Detection Methods
- Autores
- McAteer, James; Aschwanden, Markus J.; Dimitropoulou, Michaila; Georgoulis, Manolis K.; Pruessner, Gunnar; Morales, Laura Fernanda; Ireland, Jack; Abramenko, Valentyna
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical methods to clearly and unambiguously detect SOC in data often leads us outside the comfort zone of our own disciplines—the answers to these questions are often obtained by studying the advances made in other fields of study. In addition, numerical detection methods often provide the optimum link between simulations and experiments in scientific research. We seek to explore this boundary where the rubber meets the road, to review this expanding field of research of numerical detection of SOC systems over the past 25 years, and to iterate forwards so as to provide some foresight and guidance into developing breakthroughs in this subject over the next quarter of a century.
Fil: McAteer, James. New Mexico State University Las Cruces;
Fil: Aschwanden, Markus J.. Lockheed Martin Corporation;
Fil: Dimitropoulou, Michaila. University Of Athens;
Fil: Georgoulis, Manolis K.. Academy Of Athens;
Fil: Pruessner, Gunnar. Imperial College London; Reino Unido
Fil: Morales, Laura Fernanda. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Ireland, Jack. Nasa Goddard Space Flight Center; Estados Unidos
Fil: Abramenko, Valentyna. Pulkovo Observatory, Russian Academy Of Sciences;; Rusia - Materia
-
NUMERICAL METHODS
SELF ORGANIZED CRITICALITY - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/79253
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25 Years of Self-organized Criticality: Numerical Detection MethodsMcAteer, JamesAschwanden, Markus J.Dimitropoulou, MichailaGeorgoulis, Manolis K.Pruessner, GunnarMorales, Laura FernandaIreland, JackAbramenko, ValentynaNUMERICAL METHODSSELF ORGANIZED CRITICALITYhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical methods to clearly and unambiguously detect SOC in data often leads us outside the comfort zone of our own disciplines—the answers to these questions are often obtained by studying the advances made in other fields of study. In addition, numerical detection methods often provide the optimum link between simulations and experiments in scientific research. We seek to explore this boundary where the rubber meets the road, to review this expanding field of research of numerical detection of SOC systems over the past 25 years, and to iterate forwards so as to provide some foresight and guidance into developing breakthroughs in this subject over the next quarter of a century.Fil: McAteer, James. New Mexico State University Las Cruces;Fil: Aschwanden, Markus J.. Lockheed Martin Corporation;Fil: Dimitropoulou, Michaila. University Of Athens;Fil: Georgoulis, Manolis K.. Academy Of Athens;Fil: Pruessner, Gunnar. Imperial College London; Reino UnidoFil: Morales, Laura Fernanda. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Ireland, Jack. Nasa Goddard Space Flight Center; Estados UnidosFil: Abramenko, Valentyna. Pulkovo Observatory, Russian Academy Of Sciences;; RusiaSpringer2015-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/79253McAteer, James; Aschwanden, Markus J.; Dimitropoulou, Michaila; Georgoulis, Manolis K.; Pruessner, Gunnar; et al.; 25 Years of Self-organized Criticality: Numerical Detection Methods; Springer; Space Science Reviews; 198; 1-4; 5-2015; 217-2660038-6308CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s11214-015-0158-7info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:42:10Zoai:ri.conicet.gov.ar:11336/79253instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:42:10.337CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
25 Years of Self-organized Criticality: Numerical Detection Methods |
title |
25 Years of Self-organized Criticality: Numerical Detection Methods |
spellingShingle |
25 Years of Self-organized Criticality: Numerical Detection Methods McAteer, James NUMERICAL METHODS SELF ORGANIZED CRITICALITY |
title_short |
25 Years of Self-organized Criticality: Numerical Detection Methods |
title_full |
25 Years of Self-organized Criticality: Numerical Detection Methods |
title_fullStr |
25 Years of Self-organized Criticality: Numerical Detection Methods |
title_full_unstemmed |
25 Years of Self-organized Criticality: Numerical Detection Methods |
title_sort |
25 Years of Self-organized Criticality: Numerical Detection Methods |
dc.creator.none.fl_str_mv |
McAteer, James Aschwanden, Markus J. Dimitropoulou, Michaila Georgoulis, Manolis K. Pruessner, Gunnar Morales, Laura Fernanda Ireland, Jack Abramenko, Valentyna |
author |
McAteer, James |
author_facet |
McAteer, James Aschwanden, Markus J. Dimitropoulou, Michaila Georgoulis, Manolis K. Pruessner, Gunnar Morales, Laura Fernanda Ireland, Jack Abramenko, Valentyna |
author_role |
author |
author2 |
Aschwanden, Markus J. Dimitropoulou, Michaila Georgoulis, Manolis K. Pruessner, Gunnar Morales, Laura Fernanda Ireland, Jack Abramenko, Valentyna |
author2_role |
author author author author author author author |
dc.subject.none.fl_str_mv |
NUMERICAL METHODS SELF ORGANIZED CRITICALITY |
topic |
NUMERICAL METHODS SELF ORGANIZED CRITICALITY |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical methods to clearly and unambiguously detect SOC in data often leads us outside the comfort zone of our own disciplines—the answers to these questions are often obtained by studying the advances made in other fields of study. In addition, numerical detection methods often provide the optimum link between simulations and experiments in scientific research. We seek to explore this boundary where the rubber meets the road, to review this expanding field of research of numerical detection of SOC systems over the past 25 years, and to iterate forwards so as to provide some foresight and guidance into developing breakthroughs in this subject over the next quarter of a century. Fil: McAteer, James. New Mexico State University Las Cruces; Fil: Aschwanden, Markus J.. Lockheed Martin Corporation; Fil: Dimitropoulou, Michaila. University Of Athens; Fil: Georgoulis, Manolis K.. Academy Of Athens; Fil: Pruessner, Gunnar. Imperial College London; Reino Unido Fil: Morales, Laura Fernanda. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina Fil: Ireland, Jack. Nasa Goddard Space Flight Center; Estados Unidos Fil: Abramenko, Valentyna. Pulkovo Observatory, Russian Academy Of Sciences;; Rusia |
description |
The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical methods to clearly and unambiguously detect SOC in data often leads us outside the comfort zone of our own disciplines—the answers to these questions are often obtained by studying the advances made in other fields of study. In addition, numerical detection methods often provide the optimum link between simulations and experiments in scientific research. We seek to explore this boundary where the rubber meets the road, to review this expanding field of research of numerical detection of SOC systems over the past 25 years, and to iterate forwards so as to provide some foresight and guidance into developing breakthroughs in this subject over the next quarter of a century. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-05 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/79253 McAteer, James; Aschwanden, Markus J.; Dimitropoulou, Michaila; Georgoulis, Manolis K.; Pruessner, Gunnar; et al.; 25 Years of Self-organized Criticality: Numerical Detection Methods; Springer; Space Science Reviews; 198; 1-4; 5-2015; 217-266 0038-6308 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/79253 |
identifier_str_mv |
McAteer, James; Aschwanden, Markus J.; Dimitropoulou, Michaila; Georgoulis, Manolis K.; Pruessner, Gunnar; et al.; 25 Years of Self-organized Criticality: Numerical Detection Methods; Springer; Space Science Reviews; 198; 1-4; 5-2015; 217-266 0038-6308 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1007/s11214-015-0158-7 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |