Automated pattern recognition to support geological mapping and exploration target generation – A case study from southern Namibia. (June 2015) - Eberle, Detlef; Hutchins, David; Das, Sonali; Majumdar, Anandamayee; Paasche, Hendrik
Highlights: Automated, multi-attribute detection tool for linear features in magnetic data developed. Fuzzy clustering of integrated airborne gamma-ray and Landsat data sets to obtain classified map. Merging set of linear features with classified map results in (pseudo-) geological map. Fast, largely automated objective method complementary to conventional geological mapping. Method well suited to areas with large airborne geophysical data sets, but poor geological mapping. Abstract: This paper demonstrates a methodology for the automatic joint interpretation of high resolution airborne geophysical and space-borne remote sensing data to support geological mapping in a largely automated, fast and objective manner. At the request of the Geological Survey of Namibia (GSN), part of the Gordonia Subprovince of the Namaqua Metamorphic Belt situated in southern Namibia was selected for this study. All data – covering an area of 120 km by 100 km in size – were gridded, with a spacing of adjacent data points of only 200 m. The data points were coincident for all data sets. Published criteria were used to characterize the airborne magnetic data and to establish a set of attributes suitable for the recognition of linear features and their pattern within the study area. This multi-attribute analysis of the airborne magnetic data provided the magnetic lineament pattern of the study area. To obtain a (pseudo-) lithology map of the area, the high resolution airborne gamma-ray data were integrated with selected Landsat band data using unsupervised fuzzy partitioning clustering. The outcome of this unsupervised clustering is a classified (zonal) map which in terms of the power of spatial resolution is superior to any regional geological mapping. The classified zones are then assigned geological/geophysical parameters and attributes known from the study area, e.g. lithology, physical rock properties, age, chemical composition, geophysical field characteristics, etc. This information is obtained from the examination of archived geological reports, borehole logs, any kind of existing geological/geophysical data and maps as well as ground truth controls where deemed necessary. To obtain a confidence measure validating the unsupervised fuzzy clustering results and receive a quality criterion of the classified zones, stepwise linear discriminant analysis was chosen. Only a small percentage (8%) of the samples was misclassified by discriminant analysis when compared to the result obtained from unsupervised fuzzy clustering. Furthermore, a comparison of the aposterior probability of class assignment with the trustworthiness values provided by fuzzy clustering also indicates only slight differences. These observed differences can be explained by the exponential class probability term which tends to deliver either fairly high or low probability values. The methodology and results presented here demonstrate that automated objective pattern recognition can essentially contribute to geological mapping of large study areas and mineral exploration target generation. This methodology is considered well suited to a number of African countries whose large territories have recently been covered by high resolution airborne geophysical data, but where existing geological mapping is poor, incomplete or outdated.