VARIFORC: An optimized protocol for calculating non-regular first-order reversal curve (FORC) diagrams

By R. Egli. 2013

330 citations recorded by the source corpus.

Abstract High-resolution first-order reversal curve (FORC) diagrams are increasingly employed to characterize magnetic minerals in sediments, and especially as a magnetofossil detection tool. Conventional processing protocols, however, are not well suited for this purpose because the opposed needs of high resolution and smoothing of noisy data over different regions of the FORC diagram cannot be reconciled. This problem is particularly severe in samples containing non-interacting single-domain particles, whose FORC signature is described by an infinitely narrow, divergent feature called a central ridge. In this paper, a new FORC processing protocol called VARIFORC (VARIable FORC smoothing) is presented for the analysis of non-regular FORC functions, such as those featuring a central ridge. This protocol is based on weighted polynomial regression of rectangular arrays of measurement points whose size is determined by the local properties of the FORC function (i.e. small arrays where high resolution is needed and vice-versa). The resulting FORC diagram is characterized by improved signal-to-noise ratios that pass significance tests over much larger domains. This enables simultaneous quantitative analysis of FORC signatures corresponding to different magnetization processes (e.g. the central ridge produced by single-domain particles and a weak, extended background due to detrital magnetic minerals). VARIFORC has been successfully tested on a magnetofossil-bearing pelagic carbonate sample that has a non-regular FORC function, and on a volcanic ash sample with a typical pseudo-single-domain signature. An unexpected minor central ridge contribution, which is invisible to traditional processing, has been detected in the volcanic ash sample. This finding demonstrates the effectiveness of the new FORC processing protocol and potential new applications of high-resolution FORC measurements.

Subfields: Artificial Intelligence, Geophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7f601bb68b2a85c0e156b78d7c58ecf2239f3510

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In Artificial Intelligence

  1. A. Roberts
  2. Xiang Zhao
  3. C. R. Pike
  4. D. Heslop

In Geophysics

  1. A. Roberts
  2. Xiang Zhao
  3. D. Heslop
  4. C. R. Pike
  5. R. Egli
  6. Joshua Feinberg
  7. D. Dunlop
  8. J. Larrasoaña

In Molecular Biology

  1. A. Roberts
  2. Xiang Zhao
  3. D. Heslop
  4. C. R. Pike
  5. R. Egli
  6. Joshua Feinberg
  7. RichardJ . Harrison
  8. D. Dunlop

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