FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks

Autores
Pei, Zhaowen; Williams, Wyn; Nagy, Lesleis; Paterson, Greig A.; Moreno Ortega, Roberto; Muxworthy, Adrian R.; Chang, Liao
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
First‐order reversal curve (FORC) diagrams are a standard rock magnetic tool for analyzing bulk magnetic hysteresis behaviors, which are used to estimate the magnetic mineralogies and magnetic domain states of grains within natural materials. However, the interpretation of FORC distributions is challenging due to complex domain‐state responses, which introduce well‐documented uncertainties and subjectivity. Here, we propose a neural network algorithm (FORCINN) to invert the size and aspect ratio distribution from measured FORC data. We trained and tested the FORCINN model using a data set of synthetic numerical FORCs for single magnetite grains with various grain‐sizes (45–400 nm) and aspect ratios (oblate and prolate grains). In addition to successfully testing against synthetic data sets, FORCINN was found to provide good estimates of the grain‐size distributions for basalt samples and identify sample size differences in marine sediments.
Fil: Pei, Zhaowen. University of Edinburgh; Reino Unido
Fil: Williams, Wyn. University of Edinburgh; Reino Unido
Fil: Nagy, Lesleis. University of Liverpool; Reino Unido
Fil: Paterson, Greig A.. University of Liverpool; Reino Unido
Fil: Moreno Ortega, Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Fil: Muxworthy, Adrian R.. Imperial College London; Reino Unido
Fil: Chang, Liao. University of Edinburgh; Reino Unido
Materia
Nanomagnetism
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/279893
Acceder
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network_name_str CONICET Digital (CONICET)
spelling FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural NetworksPei, ZhaowenWilliams, WynNagy, LesleisPaterson, Greig A.Moreno Ortega, RobertoMuxworthy, Adrian R.Chang, LiaoNanomagnetismhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1First‐order reversal curve (FORC) diagrams are a standard rock magnetic tool for analyzing bulk magnetic hysteresis behaviors, which are used to estimate the magnetic mineralogies and magnetic domain states of grains within natural materials. However, the interpretation of FORC distributions is challenging due to complex domain‐state responses, which introduce well‐documented uncertainties and subjectivity. Here, we propose a neural network algorithm (FORCINN) to invert the size and aspect ratio distribution from measured FORC data. We trained and tested the FORCINN model using a data set of synthetic numerical FORCs for single magnetite grains with various grain‐sizes (45–400 nm) and aspect ratios (oblate and prolate grains). In addition to successfully testing against synthetic data sets, FORCINN was found to provide good estimates of the grain‐size distributions for basalt samples and identify sample size differences in marine sediments.Fil: Pei, Zhaowen. University of Edinburgh; Reino UnidoFil: Williams, Wyn. University of Edinburgh; Reino UnidoFil: Nagy, Lesleis. University of Liverpool; Reino UnidoFil: Paterson, Greig A.. University of Liverpool; Reino UnidoFil: Moreno Ortega, Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaFil: Muxworthy, Adrian R.. Imperial College London; Reino UnidoFil: Chang, Liao. University of Edinburgh; Reino UnidoAmerican Geophysical Union2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/279893Pei, Zhaowen; Williams, Wyn; Nagy, Lesleis; Paterson, Greig A.; Moreno Ortega, Roberto; et al.; FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks; American Geophysical Union; Geophysical Research Letters; 52; 3; 2-2025; 1-110094-8276CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL112769info:eu-repo/semantics/altIdentifier/doi/10.1029/2024GL112769info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-26T10:22:15Zoai:ri.conicet.gov.ar:11336/279893instacron: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:34982026-02-26 10:22:15.91CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
title FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
spellingShingle FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
Pei, Zhaowen
Nanomagnetism
title_short FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
title_full FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
title_fullStr FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
title_full_unstemmed FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
title_sort FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
dc.creator.none.fl_str_mv Pei, Zhaowen
Williams, Wyn
Nagy, Lesleis
Paterson, Greig A.
Moreno Ortega, Roberto
Muxworthy, Adrian R.
Chang, Liao
author Pei, Zhaowen
author_facet Pei, Zhaowen
Williams, Wyn
Nagy, Lesleis
Paterson, Greig A.
Moreno Ortega, Roberto
Muxworthy, Adrian R.
Chang, Liao
author_role author
author2 Williams, Wyn
Nagy, Lesleis
Paterson, Greig A.
Moreno Ortega, Roberto
Muxworthy, Adrian R.
Chang, Liao
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Nanomagnetism
topic Nanomagnetism
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv First‐order reversal curve (FORC) diagrams are a standard rock magnetic tool for analyzing bulk magnetic hysteresis behaviors, which are used to estimate the magnetic mineralogies and magnetic domain states of grains within natural materials. However, the interpretation of FORC distributions is challenging due to complex domain‐state responses, which introduce well‐documented uncertainties and subjectivity. Here, we propose a neural network algorithm (FORCINN) to invert the size and aspect ratio distribution from measured FORC data. We trained and tested the FORCINN model using a data set of synthetic numerical FORCs for single magnetite grains with various grain‐sizes (45–400 nm) and aspect ratios (oblate and prolate grains). In addition to successfully testing against synthetic data sets, FORCINN was found to provide good estimates of the grain‐size distributions for basalt samples and identify sample size differences in marine sediments.
Fil: Pei, Zhaowen. University of Edinburgh; Reino Unido
Fil: Williams, Wyn. University of Edinburgh; Reino Unido
Fil: Nagy, Lesleis. University of Liverpool; Reino Unido
Fil: Paterson, Greig A.. University of Liverpool; Reino Unido
Fil: Moreno Ortega, Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Fil: Muxworthy, Adrian R.. Imperial College London; Reino Unido
Fil: Chang, Liao. University of Edinburgh; Reino Unido
description First‐order reversal curve (FORC) diagrams are a standard rock magnetic tool for analyzing bulk magnetic hysteresis behaviors, which are used to estimate the magnetic mineralogies and magnetic domain states of grains within natural materials. However, the interpretation of FORC distributions is challenging due to complex domain‐state responses, which introduce well‐documented uncertainties and subjectivity. Here, we propose a neural network algorithm (FORCINN) to invert the size and aspect ratio distribution from measured FORC data. We trained and tested the FORCINN model using a data set of synthetic numerical FORCs for single magnetite grains with various grain‐sizes (45–400 nm) and aspect ratios (oblate and prolate grains). In addition to successfully testing against synthetic data sets, FORCINN was found to provide good estimates of the grain‐size distributions for basalt samples and identify sample size differences in marine sediments.
publishDate 2025
dc.date.none.fl_str_mv 2025-02
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/11336/279893
Pei, Zhaowen; Williams, Wyn; Nagy, Lesleis; Paterson, Greig A.; Moreno Ortega, Roberto; et al.; FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks; American Geophysical Union; Geophysical Research Letters; 52; 3; 2-2025; 1-11
0094-8276
CONICET Digital
CONICET
url http://hdl.handle.net/11336/279893
identifier_str_mv Pei, Zhaowen; Williams, Wyn; Nagy, Lesleis; Paterson, Greig A.; Moreno Ortega, Roberto; et al.; FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks; American Geophysical Union; Geophysical Research Letters; 52; 3; 2-2025; 1-11
0094-8276
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL112769
info:eu-repo/semantics/altIdentifier/doi/10.1029/2024GL112769
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Geophysical Union
publisher.none.fl_str_mv American Geophysical Union
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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score 13.176822

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