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

• The thermodynamic parameters of fluid inclusion data have been estimated three dimensionally using the SVR method.
• The predictive model has been determined based on the favorable thermodynamic conditions for copper deposition.
• Pattern of additional drillholes have been determined based on the extension of high potential zones in the predictive model
• The proposed method has been evaluated in Sungun porphyry copper deposit.

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