An Automatic Model Selection‐Based Machine Learning Framework to Estimate FORC Distributions

Heslop, D. and Roberts, A. P. and Oda, H. and Zhao, X. and Harrison, R. J. and Muxworthy, A. R. and Hu, P.‐X. and Sato, T. (2020) An Automatic Model Selection‐Based Machine Learning Framework to Estimate FORC Distributions. Journal of Geophysical Research: Solid Earth, 125 (10). ISSN 2169-9313 DOI https://doi.org/10.1029/2020JB020418

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Abstract

First‐order reversal curve (FORC) distributions are a powerful diagnostic tool for characterizing and quantifying magnetization processes in fine magnetic particle systems. Estimation of FORC distributions requires the computation of the second‐order mixed derivative of noisy magnetic hysteresis data. This operation amplifies measurement noise, and for weakly magnetic systems, it can compromise estimation of a FORC distribution. Previous processing schemes, which are based typically on local polynomial regression, have been developed to smooth FORC data to suppress detrimental noise. Importantly, the smoothed FORC distribution needs to be consistent with the measurement data from which it was estimated. This can be a challenging task even for expert users, who must adjust subjectively parameters that define the form and extent of smoothing until a “satisfactory” FORC distribution is obtained. For nonexpert users, estimation of FORC distributions using inappropriate smoothing parameters can produce distorted results corrupted by processing artifacts, which can lead to spurious inferences concerning the magnetic system under investigation. We have developed a statistical machine learning framework based on a probabilistic model comparison to guide the estimation of FORC distributions. An intuitive approach is presented that reveals regions of a FORC distribution that may have been smoothed inappropriately. An associated metric can also be used to compare data preparation and local regression schemes to assess their suitability for processing a given FORC data set. Ultimately, our approach selects FORC smoothing parameters in a probabilistic fashion, which automates the derivative estimation process regardless of user expertise.

Item Type: Article
Uncontrolled Keywords: 2020AREP; IA76
Subjects: 03 - Mineral Sciences
Divisions: 03 - Mineral Sciences
08 - Green Open Access
Journal or Publication Title: Journal of Geophysical Research: Solid Earth
Volume: 125
Identification Number: https://doi.org/10.1029/2020JB020418
Depositing User: Sarah Humbert
Date Deposited: 24 Oct 2020 17:56
Last Modified: 17 Apr 2021 00:01
URI: http://eprints.esc.cam.ac.uk/id/eprint/4898

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