Proposing drilling locations based on the 3D modeling results of fluid inclusion data using the support vector regression method

摘要: Abstract In recent years, effort has been made to make the results of fluid inclusion studies applicable and use them in design exploration process. It tried this paper propose a mineralization predictive model for chalcopyrite deposition based on favorable thermodynamic conditions using 3D modeling data. To study applicability efficiency proposed method, Sungun porphyry copper deposit, East Azarbaijan province, Iran, was studied as case data prepared support vector regression method. The precisions estimation including homogenization temperature, eutectic temperature salinity were respectively 76, 71, 93%. deposition's (a range 300–400 °C moderate-to-high salinity). A comparison with that grade shows high conformity two models. drilling pattern then investigated showed there would be an almost 6% cost reduction (i.e. elimination 9 drillholes) if drillholes.