气候变化领域集成服务门户(CCPortal): An Automatic Model Selection-Based Machine Learning Framework to Estimate FORC Distributions

CCPortal

DOI	10.1029/2020JB020418
	An Automatic Model Selection-Based Machine Learning Framework to Estimate FORC Distributions
	Heslop D.; Roberts A.P.; Oda H.; Zhao X.; Harrison R.J.; Muxworthy A.R.; Hu P.-X.; Sato T.
发表日期	2020
ISSN	21699313
卷号	125 期号:10
英文摘要	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. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	First-order reversal curves; Machine learning; Rock magnetism
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187535
作者单位	Research School of Earth Sciences, Australian National University, Canberra, Australian Capital Territory, Australia; Research Institute of Geology and Geoinformation, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Department of Earth Sciences, University of Cambridge, Cambridge, United Kingdom; Department of Earth Science and Engineering, Imperial College London, South Kensington Campus, London, United Kingdom; Earthquake Research Institute (ERI), University of Tokyo, Tokyo, Japan
推荐引用方式 GB/T 7714	Heslop D.,Roberts A.P.,Oda H.,et al. An Automatic Model Selection-Based Machine Learning Framework to Estimate FORC Distributions[J],2020,125(10).
APA	Heslop D..,Roberts A.P..,Oda H..,Zhao X..,Harrison R.J..,...&Sato T..(2020).An Automatic Model Selection-Based Machine Learning Framework to Estimate FORC Distributions.Journal of Geophysical Research: Solid Earth,125(10).
MLA	Heslop D.,et al."An Automatic Model Selection-Based Machine Learning Framework to Estimate FORC Distributions".Journal of Geophysical Research: Solid Earth 125.10(2020).