Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-14T11:27:08.488Z Has data issue: false hasContentIssue false
  • English
  • Français

Image analysis of petrographic textures and fabrics using semivariance

Published online by Cambridge University Press:  05 July 2018

Andrew R. H. Swan and
Jane A. Garratt
Andrew R. H. Swan
Affiliation:
School of Geological Sciences, Kingston University, Penrhyn Road, Kingston upon Thames, Surrey, KT1 2EE, UK
Jane A. Garratt
Affiliation:
School of Geological Sciences, Kingston University, Penrhyn Road, Kingston upon Thames, Surrey, KT1 2EE, UK
Get accessRights & Permissions [Opens in a new window]

Abstract

Many subjective descriptive terms such as ‘smooth’, ‘coarse’ and ‘anisotropic’ are routinely used to convey properties of textures and fabrics, but these aspects of geological objects are not easily quantifiable. The use of directional semivariograms of image greylevels is assessed for this purpose. These quantify anisotropy of ‘coarseness/fineness’ by using semivariogram ranges, and ‘roughness/smoothness’ by using semivariogram sill values. These can be summarized by ellipses derived from semivariogram ranges and directions. The method will find applications in analysis of rock thin sections as well as analysis of surface texture of grains and microfossils.

Keywords

image analysispetrographic texturesfabricsemivariance

Information

Type
Image analysis
Information
Mineralogical Magazine , Volume 59 , Issue 395 , June 1995, pp. 189 - 196
Copyright
Copyright © The Mineralogical Society of Great Britain and Ireland 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Bai, X., Smart, P. and Leng, X. (1994) Polarizing microphotometric analysis. Geotechnique, 44, 4, 175-80.CrossRefGoogle Scholar
Bardossy, A. and Bardossy, G. (1984) Comparison of geostatistical calculations with the results of open pit mining at the Iharkut Bauxite district, Hungary: a case study. Mathematical Geol., 16, 173-91.CrossRefGoogle Scholar
Davis, J.C. (1986) Statistics and Data Analysis in Geology. (2nd Edition). Wiley. 646 pp.Google Scholar
Davis, L.S., Johns, S.A. and Aggarwal, J.K. (1979) Texture analysis using generalized co-occurrence matrices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1, 251-9.CrossRefGoogle Scholar
Derbyshire, E., Unwin, D.J., Fang, X.M. and Langford, M. (1992) The Fourier frequency-domain representation of sediment fabric anisotropy. Computers Geosci., 18, 63-73.CrossRefGoogle Scholar
Erslev, E.A. (1988) Normalized center-to-center strain analysis of packed aggregates. J. Struct. Geol., 10, 201-9.CrossRefGoogle Scholar
Erslev, E.A. and Ge, H. (1990) Least-squares center-tocenter and mean object ellipse fabric analysis. J. Struct. Geol., 12, 1047-59.CrossRefGoogle Scholar
Fry, N. (1979) Random point distributions and strainry, N. (1979) Random point distributions and strain measurement in rocks. Tectonophys., 60, 89-105.CrossRefGoogle Scholar
Hanna, S.S. and Fry, N. (1979) A comparison of methods of strain determination in rocks from southwest Dyfed (Pembrokeshire) and adjacent areas. J. Struct. Geol., 1, 155-62.CrossRefGoogle Scholar
Haralick, R.M., Shanmugam, K. and Dinstein, I. (1973) Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3, 610-21.CrossRefGoogle Scholar
Lisle, R.J. (1985) Geological Strain Analysis: A Manual for the Rf/ϕ Technique. Pergamon Press, Oxford.Google Scholar
Matheron, G. (1963) Principles of geostatistics. Econ. Geol., 58, 1246-66.CrossRefGoogle Scholar
Peleg, S., Naor, J. and Avnir, D. (1984) Multiple resolution texture analysis and classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 518-23.CrossRefGoogle Scholar
Pfleiderer, S. and Halls, H.C. (1993) Magnetic pore fabric analysis: verification through image autocorrelation. J. Geophys. Res., 98, B3, 4311-6.CrossRefGoogle Scholar
Pfleiderer, S., Ball, D. G. A. and Bailey, R.C. (1993) AUTO: a computer program for the determination of the two-dimensional autocorrelation function of digital images. Computers Geosci., 19, 825-9.CrossRefGoogle Scholar
Ramsay, J.G. and Huber, M.I. (1983) The Techniques of Modern Structural Geology. Volume 1: Strain Analysis. Academic Press, London.Google Scholar
Smart, P. (1966) Optical microscopy and soil structure. Nature. 210, 1400.CrossRefGoogle Scholar
Tomita, F., Shirai, Y. and Tsuji, S. (1982) Description of textures by structural analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 4, 183-91.CrossRefGoogle Scholar
Weszka, J.S., Dyer, C.D. and Rosenfeld, A. (1976) A comparative study of texture measures for terrain classification. IEEE Transactions on Systems, Man and Cybernetics. 6, 269-85.CrossRefGoogle Scholar