FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks
Abstract 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 distribution...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-02-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2024GL112769 |
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| _version_ | 1850053576159657984 |
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| author | Zhaowen Pei Wyn Williams Lesleis Nagy Greig A. Paterson Roberto Moreno Adrian R. Muxworthy Liao Chang |
| author_facet | Zhaowen Pei Wyn Williams Lesleis Nagy Greig A. Paterson Roberto Moreno Adrian R. Muxworthy Liao Chang |
| author_sort | Zhaowen Pei |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-cd58114e68124420bafaaed0ce031f23 |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-cd58114e68124420bafaaed0ce031f232025-08-20T02:52:30ZengWileyGeophysical Research Letters0094-82761944-80072025-02-01523n/an/a10.1029/2024GL112769FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural NetworksZhaowen Pei0Wyn Williams1Lesleis Nagy2Greig A. Paterson3Roberto Moreno4Adrian R. Muxworthy5Liao Chang6School of GeoSciences The University of Edinburgh Edinburgh UKSchool of GeoSciences The University of Edinburgh Edinburgh UKDepartment of Earth Ocean and Ecological Sciences The University of Liverpool Liverpool UKDepartment of Earth Ocean and Ecological Sciences The University of Liverpool Liverpool UKSchool of GeoSciences The University of Edinburgh Edinburgh UKDepartment of Earth Science and Engineering Imperial College London London UKLaboratory of Orogenic Belts and Crustal Evolution School of Earth and Space Sciences Peking University Beijing P. R. ChinaAbstract 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.https://doi.org/10.1029/2024GL112769 |
| spellingShingle | Zhaowen Pei Wyn Williams Lesleis Nagy Greig A. Paterson Roberto Moreno Adrian R. Muxworthy Liao Chang FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks Geophysical Research Letters |
| title | 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_short | FORCINN: First‐Order Reversal Curve Inversion of Magnetite Using Neural Networks |
| title_sort | forcinn first order reversal curve inversion of magnetite using neural networks |
| url | https://doi.org/10.1029/2024GL112769 |
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