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 - Paasche, Hendrik A1 - Eberle, Detlef T1 - Automated compilation of pseudo-lithology maps from geophysical data sets a comparison of Gustafson-Kessel and fuzzy c-means cluster algorithms JF - Exploration geophysics : the bulletin of the Australian Society of Exploration Geophysicists N2 - 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. KW - cluster analysis KW - data integration KW - airborne KW - South Africa KW - Gustafson-Kessel KW - fuzzy c-means Y1 - 2011 U6 - https://doi.org/10.1071/EG11014 SN - 0812-3985 VL - 42 IS - 4 SP - 275 EP - 285 PB - CSIRO CY - Collingwood 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 -