Rock magnetism is useful in
various applications. Hematite is one of the two most important carriers of
magnetism in the natural world and its magnetic features were mostly studied
through laboratory experiments using synthetic hematite samples. A gap exists
between the magnetic behaviors of hematite contained in the natural rocks and
ores and those of synthetic hematite samples. This paper presents the results
of a rock magnetism study on the natural hematite ores from the Whaleback mine
in the Hamersley Province in the northwest of Western Australia. It was found
that high-grade hematite ores carry a much higher remanent magnetization than
induced magnetization. Hematite ores with less than 0.1% magnetite appear to
have an exponential correlation between the bulk susceptibility and hematite
content in weight percentage, different from the commonly accepted linear
relationship between the bulk susceptibility and hematite content obtained from
synthetic hematite samples. The new knowledge gained from this study
contributes to a better understanding of magnetic behaviors of hematite,
particularly natural hematite, and hence applications to other relevant disciplines.
References
[1]
Borradaile, G.J. and Henry, B. (1997) Tectonic Application of Magnetic Susceptibility and Its Anisotropy. Earth- Science Reviews, 42, 49-93. http://dx.doi.org/10.1016/S0012-8252(96)00044-X
[2]
Borradaile, G.J. (1988) Magnetic Susceptibility, Petrofabrics and Strain. Tectonophysics, 156, 1-20.
http://dx.doi.org/10.1016/0040-1951(88)90279-X
[3]
Guo, W. (1999) Magnetic Petrophysics and Density Investigations of the Hamersley Province, Western Australia: Implications for Magnetic and Gravity Interpretation. The University of Western Australia, Perth.
[4]
Guo, W.W. Magnetic Mineralogical Characteristics of Hamersley Iron Ores in Western Australia. Journal of Applied Mathematics and Physics. (In Press)
[5]
Hounslow, M.W. and Maher, B.A. (1999) Source of the Climate Signal Recorded by Magnetic Susceptibility Variations in Indian Ocean Sediments. Journal of Geophysical Research, 104, 5047-5061.
http://dx.doi.org/10.1029/1998JB900085
[6]
Balla Ondoa, A., Ngos III, S., Ndjeng, E., Abolo, A. and N’Nanga, A. (2014) Contribution of the Magnetic Susceptibility to the Characterization of the Babouri-Figuil Cretaceous Basin. Open Journal of Soil Science, 4, 272-283.
http://dx.doi.org/10.4236/ojss.2014.48029
[7]
Thompson, R. and Oldfield, E. (1986) Environmental Magnetism. Allen and Unwin, London.
http://dx.doi.org/10.1007/978-94-011-8036-8
[8]
Guo, W., Dentith, M.C., Bird, R.T. and Clark, D.A. (2001) Syste-matic Error Analysis of Demagnetisation and Implications for Magnetic Interpretation. Geophysics, 66, 562-570. http://dx.doi.org/10.1190/1.1444947
[9]
Guo, W.W., Li, Z.X. and Dentith, M.C. (2011) Magnetic Petrophysical Results from the Hamersley Basin and Their Implications for Interpretation of Magnetic Surveys. Australian Journal of Earth Sciences, 58, 317-333.
http://dx.doi.org/10.1080/08120099.2011.552984
[10]
Chen, C.W. (1977) Magnetism and Metallurgy of Soft Magnetic Materials. North-Holland, Amsterdam.
[11]
Merrill, R.T., McElhinny, M.W. and McFadden, P.L. (1996) The Magnetic Field of the Earth: Paleomagnetism, the Core, and the Deep Mantle. Academic Press, San Diego.
[12]
Tarling, D.H. and Hrouda, F. (1993) The Magnetic Anisotropy of Rocks. Chapman & Hall, London.
[13]
Dunlop, D.J. and Ozdemir, O. (1997) Rock Magnetism. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511612794
Clark, D.A. and Schmidt, P. (1986) Magnetic Properties of the Banded-Iron Formations of the Hamersley Group, WA. CSIRO Division of Mineral Physics, AMIRA Report 1638.
[16]
Guo, W.W. (2010) A Novel Application of Neural Networks for Instant Iron-Ore Grade Estimation. Expert Systems with Applications, 37, 8729-8735. http://dx.doi.org/10.1016/j.eswa.2010.06.043
[17]
Li, M.M., Guo, W., Verma, B., Tickle, K. and O’Connor, J. (2009) Intelligent Methods for Solving Inverse Problems of Backscattering Spectra with Noise: A Comparison between Neural Networks and Simulated Annealing. Neural Computing and Applications, 18, 423-430. http://dx.doi.org/10.1007/s00521-008-0219-x