An Improved Algorithm For Unmixing FirstāOrder Reversal Curve Diagrams Using Principal Component Analysis
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Firstāorder reversal curve (FORC) diagrams of synthetic binary mixtures with singleādomain, vortex state, and multiādomain end members (EMs) were analyzed using principal component analysis (FORCāPCA). Mixing proportions derived from FORCāPCA 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 FORCāPCA 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 FORCāPCA with examples of quantitative unmixing of magnetic components that will have widespread applicability in paleomagnetism and environmental magnetism.
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1525-2027