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

作者：Zhaowen Pei, Wyn Williams, Lesleis Nagy, Greig A. Paterson, Roberto Moreno, Adrian R. Muxworthy, Liao Chang · 发表于：Geophysical Research Letters · 年份：2025 · DOI：10.1029/2024gl112769 · 被引用次数：3 · 研究领域：Geomagnetism and Paleomagnetism Studies、Geophysical and Geoelectrical Methods、Characterization and Applications of Magnetic Nanoparticles

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.