TY  - JOUR
A1  - Paasche, Hendrik
A1  - Eberle, Detlef G.
T1  - Rapid integration of large airborne geophysical data suites using a fuzzy partitioning cluster algorithm : a tool for geological mapping and mineral exploration targeting
N2  - Unsupervised classification techniques, such as cluster algorithms, are routinely used for structural exploration and integration of multiple frequency bands of remotely sensed spectral datasets. However, up to now, very few attempts have been made towards using unsupervised classification techniques for rapid, automated, and objective information extraction from large airborne geophysical data suites. We employ fuzzy c-means (FCM) cluster analysis for the rapid and largely automated integration of complementary geophysical datasets comprising airborne radiometric and magnetic as well as ground-based gravity data, covering a survey area of approximately 5000 km(2) located 100 km east- south-east of Johannesburg, South Africa, along the south-eastern limb of the Bushveld layered mafic intrusion complex. After preparatory data processing and normalisation, the three datasets are subjected to FCM cluster analysis, resulting in the generation of a zoned integrated geophysical map delineating distinct subsurface units based on the information the three input datasets carry. The fuzzy concept of the cluster algorithm employed also provides information about the significance of the identified zonation. According to the nature of the input datasets, the integrated zoned map carries information from near-surface depositions as well as rocks underneath the sediment cover. To establish a sound geological association of these zones we refer the zoned geophysical map to all available geological information, demonstrating that the zoned geophysical map as obtained from FCM cluster analysis outlines geological units that are related to Bushveld-type, other Proterozoic- and Karoo-aged rocks.
Y1  - 2009
UR  - http://www.publish.csiro.au/nid/224.htm
U6  - https://doi.org/10.1071/Eg08028
SN  - 0812-3985
ER  - 
TY  - JOUR
A1  - Eberle, Detlef G.
A1  - Paasche, Hendrik
T1  - Integrated data analysis for mineral exploration A case study of clustering satellite imagery, airborne gamma-ray, and regional geochemical data suites
JF  - Geophysics
N2  - 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 3700 km(2) 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.
Y1  - 2012
U6  - https://doi.org/10.1190/GEO2011-0063.1
SN  - 0016-8033
VL  - 77
IS  - 4
SP  - B167
EP  - B176
PB  - Society of Exploration Geophysicists
CY  - Tulsa
ER  -