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Geochemical patterns of schists from the Bushmanland Group: An artificial neural networks approach

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2006-12
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Lacassie Reyes, Juan Pablo
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Geochemical patterns of schists from the Bushmanland Group: An artificial neural networks approach
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  • Lacassie Reyes, Juan Pablo;
  • McClung, C. R.;
  • Bailie, R. H.;
  • Gutzmer, J.;
  • Ruiz del Solar, Javier;
Abstract
The Mesoproterozoic Bushmanland Group is situated in the central region of the 1000 to 1200 Ma Namaqualand Metamorphic Complex (NMC). The NMC comprises a belt of highly deformed medium- to high-grade metamorphic rocks to the west of the Archean Kaapvaal Craton of southern Africa. The Bushmanland Group, one of the many supracrustal sequences that make up the NMC, is a metavolcano-sedimentary succession that hosts economically significant concentrations of sillimanite and base-metal sulfide deposits. The present investigation was carried out to study the geochemistry of a large set of representative samples of psammo-pelitic schists from the Bushmanland Group, which includes data from three different schist units: Namies Schist Formation, Shaft Schist Formation and Ore Equivalent Schist. The objective was three-fold: to test the lateral correlatability of these schist units as determined by field relationships, to identify the geochemical signature of the schists and to test the validity of an Artificial Neural Network approach as an exploration tool. Two multidimensional datasets, respectively comprising 10 major and 18 trace elements, were constructed using selected published schist analyses. Both schist datasets were analyzed using self-organizing neural maps for visualizing and clustering high-dimensional geochemical data. Geochemical differences between the various schists were visualized using colored two-dimensional maps that can be visually and quantitatively interpreted. The results of this study confirm the lateral correlatability of the schist units evaluated in this communication. It was also found that each schist unit or portions of them represent a distinct geochemical signature that is related to true lithological variations. The results show that the Artificial Neural Network approach can be used as a powerful tool for regional mineral exploration in poly-deformed and metamorphosed terrains where identification of stratigraphic units through lateral correlation by means of fieldwork and petrography remains highly speculative.
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URI: https://repositorio.uchile.cl/handle/2250/124903
ISSN: 0375-6742
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JOURNAL OF GEOCHEMICAL EXPLORATION Volume: 91 Issue: 1-3 Pages: 81-98 Published: OCT-DEC 2006
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