Detlef G. Eberle

Rapid integration of large airborne geophysical data suites using a fuzzy partitioning cluster algorithm: a tool for geological mapping and mineral exploration targeting

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 km2 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.

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

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. Fuzzy Gustafson–Kessel cluster analysis is employed to integrate suites of disparate data sets. The fuzzy c-means algorithm is used as a reference to discuss the advantages of the Gustafson–Kessel algorithm and revise a database comprising airborne and ground-based geophysical data sets while minimising preparatory data processing required for fuzzy c-means cluster analysis.

Are Earth Sciences lagging behind in data integration methodologies

This article reflects discussions German and South African Earth scientists, statisticians and risk analysts had on occasion of two bilateral workshops on Data Integration Technologies for Earth System Modelling and Resource Management. The workshops were held in October 2012 at Leipzig, Germany, and April 2013 at Pretoria, South Africa, and were attended by about 70 researchers, practitioners and data managers of both countries. Both events were arranged as part of the South African-German Year of Science 2012/2013. The South African National Research Foundation (NRF, UID 81579) has supported the two workshops as part of the South African–German Year of Science activities 2012/2013 established by the German Federal Ministry of Education and Research and the South African Department of Science and Technology.

Integration of Satellite Imagery, Airborne Radiometric and Regional Geochemical Data Sets for Mineral Exploration

Cluster analysis algorithms enable the rapid and objective integration of multi-method data bases with unknown parameter relationship between the individual data types present in the data base. We are employing the fuzzy Gustafson-Kessel (GK) cluster algorithm to integrate a data base comprising 2D information from Landsat satellite imagery, airborne radiometric and regional geochemical data acquired over a survey area in the Northern Cape Province of South Africa. We are combining the structural information provided by satellite and airborne radiometric data with regional geochemical soil sample data to obtain an objective 2D classified zoned map reflecting sub-surface lithology. Ground truth control supports to ascribe the various clusters to lithology and generate mineral exploration target areas.

Integration and Data-driven Zonation of Large Airborne Geophysical Maps Using Cluster Analysis

Since the advent of modern desktop computers few attempts have been made towards rapid, automated and objective information extraction from large geophysical data suites. We employ fuzzy c-means (FCM) cluster analysis for the rapid and objective integration of disparate geophysical data sets comprising airborne radiometric and magnetic as well as ground-based gravity data covering a survey area of approximately 5000 km2 located 100 km east-south-east of Johannesburg, South Africa. After preparatory data processing and normalization, the three data sets are subjected to FCM cluster analysis resulting in the generation of a zoned integrated geophysical map delineating distinguished subsurface units based on the information the three input data sets carry. 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 which are related to Bushveld-type, other Proterozoic- and Karoo-aged rocks.

Recovery, compilation, back-calibration, and standardization of existing radiometric survey data: Namibia, southern Africa

During 1992 and 1993 select portions of existing government airborne radiometric data covering almost 91,000 km{sup 2} of central Namibia were compiled into a master digital data set. This compilation involved the interactive, semi-automated digital recovery of approximately 42,000 line kilometers of original analogue chart traces. A further 49,000 line kilometers of digital data were also reprocessed. Available data represented ten (10) different surveys collected over twelve (12) years with a variety of spectrometers, spectral windows and survey parameters. Preliminary digital grids of each radioelement were compiled, verified and used to select representative sites for ground measurements within each survey block. Results obtained from the ground program were used to back-calibrate the airborne data, standardize the various surveys and convert airborne measurements into equivalent ground concentrations of uranium, thorium and potassium. The quality and consistency of final map products conclusively demonstrates that existing analogue radiometric data, in various states of preservation, can be successfully recovered, combined with ``modern`` digital data, and utilized to assist exploration, mapping and environmental studies.