An Improved Algorithm for Unmixing First-Order Reversal Curve Diagrams Using Principal Component Analysis

First-order reversal curve (FORC) diagrams of synthetic binary mixtures with single-domain, vortex state, and multidomain end-members (EMs) were analyzed using principal component analysis (FORC-PCA). Mixing proportions derived from FORC-PCA are shown to deviate systematically from the known weight percent of EMs, which is caused by the lack of reversible magnetization contributions to the FORC distribution. The error in the mixing proportions can be corrected by applying PCA to the raw FORCs, rather than to the processed FORC diagram, thereby capturing both reversible and irreversible contributions to the signal. Here we develop a new practical implementation of the FORC-PCA method that enables quantitative unmixing to be performed routinely on suites of FORC diagrams with up to four distinct EMs. The method provides access not only to the processed FORC diagram of each EM, but also to reconstructed FORCs, which enables objective criteria to be defined that aid identification of physically realistic EMs. We illustrate FORC-PCA with examples of quantitative unmixing of magnetic components that will have widespread applicability in paleomagnetism and environmental magnetism.