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FORCinel: An improved algorithm for calculating first-order reversal curve distributions using locally weighted regression smoothing

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Abstract

[1] We describe a modification to existing algorithms for the calculation of first-order reversal curve (FORC) diagrams using locally weighted regression smoothing (often referred to as “LOESS” smoothing). The new algorithm offers several advantages over current methods: (1) it allows the FORC distribution to be calculated using a constant smoothing factor all the way to the Hc = 0 axis; (2) noninteger values of the smoothing factor can be specified, enabling finer control over the degree of smoothing and the development of a graphical method for automated selection of the optimum smoothing factor; (3) it performs automated extrapolation across gaps or undefined regions of FORC space. This has two applications: first, bad curves or outlying data points caused by instrumental instabilities can be removed from the data, eliminating artifacts from the final FORC diagram; second, specific regions of interest in the FORC measurement can be masked out in order to investigate their contribution to the final FORC diagram. The new algorithm forms the basis of FORCinel, a new user-friendly suite of FORC analysis tools with graphical user interface.

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