Exploration Geophysics Exploration Geophysics Society
Journal of the Australian Society of Exploration Geophysicists
RESEARCH ARTICLE

Automated compilation of pseudo-lithology maps from geophysical data sets: a comparison of Gustafson-Kessel and fuzzy c-means cluster algorithms

Hendrik Paasche 1 3 Detlef Eberle 2
+ Author Affiliations
- Author Affiliations

1 University of Potsdam, Institute of Earth and Environmental Sciences, Karl-Liebknecht-Str. 24, 14476 Potsdam-Golm, Germany.

2 Council for Geoscience, Geophysics Business Unit, Private Bag X112, Pretoria 0001, South Africa.

3 Corresponding author. Email: hendrik@geo.uni-potsdam.de

Exploration Geophysics 42(4) 275-285 https://doi.org/10.1071/EG11014
Submitted: 15 March 2011  Accepted: 22 September 2011   Published: 8 November 2011

Abstract

The fuzzy partitioning Gustafson-Kessel cluster algorithm is employed for rapid and objective integration of multi-parameter Earth-science related databases. We begin by evaluating the Gustafson-Kessel algorithm using the example of a synthetic study and compare the results to those obtained from the more widely employed fuzzy c-means algorithm. Since the Gustafson-Kessel algorithm goes beyond the potential of the fuzzy c-means algorithm by adapting the shape of the clusters to be detected and enabling a manual control of the cluster volume, we believe the results obtained from Gustafson-Kessel algorithm to be superior. Accordingly, a field database comprising airborne and ground-based geophysical data sets is analysed, which has previously been classified by means of the fuzzy c-means algorithm. This database is integrated using the Gustafson-Kessel algorithm thus minimising the amount of empirical data processing required before and after fuzzy c-means clustering. The resultant zonal geophysical map is more evenly clustered matching regional geology information available from the survey area. Even additional information about linear structures, e.g. as typically caused by the presence of dolerite dykes or faults, is visible in the zonal map obtained from Gustafson-Kessel cluster analysis.

Key words: cluster analysis, data integration, airborne, South Africa, Gustafson-Kessel, fuzzy c-means.


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