In this section
@article{Pei:2025:10.1029/2024gl112769,
author = {Pei, Z and Williams, W and Nagy, L and Paterson, GA and Moreno, R and Muxworthy, AR and Chang, L},
doi = {10.1029/2024gl112769},
journal = {Geophysical Research Letters},
title = {FORCINN: FirstOrder Reversal Curve Inversion of Magnetite Using Neural Networks},
url = {http://dx.doi.org/10.1029/2024gl112769},
volume = {52},
year = {2025}
}
TY - JOUR
AB - <jats:title>Abstract</jats:title><jats:p>Firstorder 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 domainstate responses, which introduce welldocumented 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 grainsizes (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 grainsize distributions for basalt samples and identify sample size differences in marine sediments.</jats:p>
AU - Pei,Z
AU - Williams,W
AU - Nagy,L
AU - Paterson,GA
AU - Moreno,R
AU - Muxworthy,AR
AU - Chang,L
DO - 10.1029/2024gl112769
PY - 2025///
SN - 0094-8276
TI - FORCINN: FirstOrder Reversal Curve Inversion of Magnetite Using Neural Networks
T2 - Geophysical Research Letters
UR - http://dx.doi.org/10.1029/2024gl112769
UR - https://doi.org/10.1029/2024gl112769
VL - 52
ER -