- journal_title：Geophysics
- Contributor：Detlef G. Eberle ; Hendrik Paasche
- Publisher：Society of Exploration Geophysicists
- Date：2012-
- Format：text/html
- Language：en
- Identifier：10.1190/geo2011-0063.1
- journal_abbrev：Geophysics
- issn：0016-8033
- volume：77
- issue：4
- firstpage：B167
- section：Case Histories
Partitioning cluster algorithms have proven to be powerful tools for data-driven integration of large geoscientific databases. We used fuzzy Gustafson-Kessel cluster analysis to integrate Landsat imagery, airborne radiometric, and regional geochemical data to aid in the interpretation of a multimethod database. The survey area extends over <mml:math><mml:mrow><mml:mn>3700</mml:mn><mml:mrow><mml:mo> </mml:mo></mml:mrow><mml:mrow><mml:mo> </mml:mo></mml:mrow><mml:msup><mml:mrow><mml:mi>km</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>3700 km2 and is located in the Northern Cape Province, South Africa. We carefully selected five variables for cluster analysis to avoid the clustering results being dominated by spatially high-correlated data sets that were present in our database. Unlike other, more popular cluster algorithms, such as k-means or fuzzy c-means, the Gustafson-Kessel algorithm requires no preclustering data processing, such as scaling or adjustment of histographic data distributions. The outcome of cluster analysis was a classified map that delineates prominent near-to-surface structures. To add value to the classified map, we compared the detected structures to mapped geology and additional geophysical ground-truthing data. We were able to associate the structures detected by cluster analysis to geophysical and geological information thus obtaining a pseudolithology map. The latter outlined an area with increased mineral potential where manganese mineralization, i.e., psilomelane, had been located.