Magnetic unmixing of first-order reversal curve diagrams using principal component analysis

We describe a quantitative magnetic unmixing method based on principal component analysis (PCA) of first-order reversal curve (FORC) diagrams. For PCA we resample FORC distributions on grids that capture diagnostic signatures of single-domain (SD), pseudo-single-domain (PSD), and multi-domain (MD) magnetite, as well as of minerals such as hematite. Individual FORC diagrams are recast as linear combinations of end-member (EM) FORC diagrams, located at user-defined positions in PCA space. The EM selection is guided by constraints derived from physical modeling and imposed by data scatter. We investigate temporal variations of two EMs in bulk North Atlantic sediment cores collected from the Rockall Trough and the Iberian Continental Margin. Sediments from each site contain a mixture of magnetosomes and granulometrically distinct detrital magnetite. We also quantify the spatial variation of three EM components (a coarse silt-sized MD component, a fine silt-sized PSD component, and a mixed clay-sized component containing both SD magnetite and hematite) in surficial sediments along the flow path of the North Atlantic Deep Water (NADW). These samples were separated into granulometric fractions, which helped constrain EM definition. PCA-based unmixing reveals systematic variations in EM relative abundance as a function of distance along NADW flow. Finally, we apply PCA to the combined dataset of Rockall Trough and NADW sediments, which can be recast as a four-EM mixture, providing enhanced discrimination between components. Our method forms the foundation of a general solution to the problem of unmixing multi-component magnetic mixtures, a fundamental task of rock magnetic studies. This article is protected by copyright. All rights reserved

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.This work was supported financially by the Australian Research Council through grant DP160100805 and by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013)/ERC grant agreement 320750

Changing sediment supply during glacial-interglacial intervals in the North Atlantic revealed by particle size characterization and environmental magnetism

The Pliocene-Pleistocene transition is characterized by an abundance of Ice-Rafted Debris (IRD) in the North Atlantic basin. One of the regions affected by IRD during this period is the Gardar Drift, where the DSDP Leg 94 Hole 611A is located. This region received sediments from different sources during the glacial and interglacial intervals (e.g., Iceland and Greenland). We analyzed grain size and particle-size specific magnetic properties of sediments for their provenance characterization between ∼2.64 and 2.52 Ma. Our results show that major proportion of bulk sediments during both glacial and interglacial periods were made up of basaltic-rich Icelandic sediments, whereas only during intense glacial periods (Marine Isotope Stages 100 and 104), a small proportion of non-basaltic sand compositions were identified, possibly sourced from Greenland and other non-basaltic provenance. The non-basaltic sand fractions during the intense glacial periods were likely supplied as IRDs. In addition, a new level of coarse lithics (38 pcs. of >1 mm) composed of different rocks types (e.g., basalt, granite, granodiorite etc.) were identified in DSDP 611A Hole during the end of MIS 104 glacial period. The coarse lithic fragments showed distinctive magnetic properties than rest of the particle sizes and were classified as Iceberg-Rafted Debris (IBRD). Overall, our results show that higher sand percentage was found during the intense glacial episodes, and their magnetic grain size analysis could help in distinguishing their provenance. We elaborate that particle size specific magnetic measurements of sand fractions could help in rapidly characterizing the glacial episodes in the subpolar North Atlantic

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

Magnetic unmixing of first-order reversal curve diagrams using principal component analysis

We describe a quantitative magnetic unmixing method based on principal component analysis (PCA) of first-order reversal curve (FORC) diagrams. For PCA we resample FORC distributions on grids that capture diagnostic signatures of single-domain (SD), pseudo-single-domain (PSD), and multidomain (MD) magnetite, as well as of minerals such as hematite. Individual FORC diagrams are recast as linear combinations of end-member (EM) FORC diagrams, located at user-defined positions in PCA space. The EM selection is guided by constraints derived from physical modeling and imposed by data scatter. We investigate temporal variations of two EMs in bulk North Atlantic sediment cores collected from the Rockall Trough and the Iberian Continental Margin. Sediments from each site contain a mixture of magnetosomes and granulometrically distinct detrital magnetite. We also quantify the spatial variation of three EM components (a coarse silt-sized MD component, a fine silt-sized PSD component, and a mixed clay-sized component containing both SD magnetite and hematite) in surficial sediments along the flow path of the North Atlantic Deep Water (NADW). These samples were separated into granulometric fractions, which helped constrain EM definition. PCA-based unmixing reveals systematic variations in EM relative abundance as a function of distance along NADW flow. Finally, we apply PCA to the combined dataset of Rockall Trough and NADW sediments, which can be recast as a four-EM mixture, providing enhanced discrimination between components. Our method forms the foundation of a general solution to the problem of unmixing multi-component magnetic mixtures, a fundamental task of rock magnetic studies.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC grant agreement 320750.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/2015GC00590