By Gabor Gaal, G. Gaal, D. F. Merriam
Quantitative source evaluate tools play an expanding function in exploration for petroleum, water and minerals. This quantity provides a global overview at the cutting-edge of the automatic technique in source exploration. The papers taken from these awarded on the symposium are labeled to both strategies, i.e., development research; category options; geostatistics; picture research; specialist systems/artificial intelligence; inventories; tomography and others, or to assets, i.e., petroleum, water, metals and non-metals
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Additional info for Computer Applications in Resource Estimation. Prediction and Assessment for Metals and Petroleum
The compositing reduced the s a m p l i n g error and increased t h e statistical stability of t h e t r a i n i n g data. T h e composite s a m p l e s of the Nordkalott project were grouped a n d t h e grouped s a m p l e s were analyzed to achieve t h e s a m e s a m p l i n g density and t h e s a m e analytical procedure for both s e t s of data. ICAP a n a l y s e s were done for both partial leach (aqua regia) a n d total dissolution (hydrofluorine a n d boric acids). The e l e m e n t s included in t h e classification w e r e for t h e partial leach Ba, Ca, Cr, F e , Mg, Μη, N a , Ρ, Ti, V, a n d Y and for the total dissolution Co, Cu, Fe, La, Li, Mn, Ni, P, Sc, Th, Ti, V, Y, a n d Zn.
T h i s is restríctive, be cause the l e a s t significant factors associated with t h e l e a s t reliable variables are i n t e r e s t i n g for the interpretation of geological processes. DISCUSSION The m e t h o d is easy to u s e because m o d e l s are created by d e m a r c a t i n g the desired training a r e a s and the s a m p l i n g material of t h e m o d e l s is of t h e s a m e type a s the s a m p l i n g material to be classified. T h e material m u s t b e selected to reflect t h e characterístic features of t h e areas, however, otherwise the m o d e l s become too general a n d t h e r e s u l t a n t areas of favorability e x t e n d too widely.
Geology, V. 8 3 , no. l , p . 18-29. A. : Artificial intelligence and statistics: Addison-Wesley, Reading, M a s s a c h u s e t t s , p. 17-55. , 1 9 8 4 , Statistical and k n o w l e d g e - b a s e d approaches to chnical decision-support s y s t e m s , with a n application in gastroen terology: Jour. Royal Statist. Soc. A, v. 1 4 7 , pt. 1, p. 35-77. , 1 9 8 8 , Spatial data integration of l a k e - s e d i m e n t geochemistry, geology a n d gold occurrences, M e g u m a Terrane, eastern N o v a Scotia: Proc.