Unlocking information about fine magnetic particle assemblages from first-order reversal curve diagrams: Recent advances

The magnetic domain state of a material determines its magnetic recording capability and magnetic properties. Constraining the domain state of magnetic components within complexly mixed natural magnetic mineral assemblages is challenging because most bulk magnetic methods do not enable component-specific domain state identification. First-order reversal curve (FORC) diagrams are the most diagnostic tool for this important endeavour. Over the last 20+ years, an extensive framework has been developed for FORC diagram interpretation. Recent years have been fertile and key developments are highlighted here. New FORC measurement types provide enhanced domain state diagnosis, including recognition of vortex state signatures and their importance in rock magnetism. FORC diagrams are also indicative of the dominant magnetic anisotropy type in a material, with multi-axial, in addition to uniaxial, anisotropy signatures recognised increasingly. A fundamental challenge in FORC data processing is to avoid emphasizing noise at the expense of signal or distorting a FORC distribution by excessive smoothing. Selection of an optimal FORC distribution that avoids over- or under-smoothing is now possible with machine learning approaches. A further new FORC measurement protocol enables identification of magnetically viscous particles and can assist in separating signals due to magnetic mineral mixtures. Furthermore, FORC unmixing for large sample sets now enables quantitative separation of magnetic mineral mixtures. Splitting of the FORC signal into remanent, induced, and transient magnetization components, each of which provides information about magnetic domain state fractions in a sample, holds potential for future single sample unmixing.