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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/79253

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spelling 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 reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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