An Automatic Model Selection‐Based Machine Learning Framework to Estimate FORC Distributions. Issue 10 (22nd October 2020)

• Record Type: Journal Article
• Authors: Heslop, D.; Roberts, A. P.; Oda, H.; Zhao, X.; Harrison, R. J.; Muxworthy, A. R.; Hu, P.‐X.; Sato, T.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-22
• Subjects: First‐order reversal curves -- Rock magnetism -- Machine learning; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2020JB020418
• Languages: English
• ISSNs: 2169-9313

Abstract

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. Key Points: We develop a probabilistic framework for estimating FORC distributions Using Bayesian model selection, we identify underfitting and overfitting of FORC distributions FORCsensei software can be used to determine smoothing parameters to estimate a FORC distribution