3D Geological Modeling And Metallogenic Information Quantitative Extraction Of Ore Deposit

With the advancement of prospecting, the available outcrop mine is becoming less and less. Thus, it is difficult to meet current demand for mineral exploration by using the traditional prospecting methods. Therefore, there is an urgent need to establish an efficient method for metallogenic prediction. Currently,3D GIS and 3D geological modeling technology have been widely used in prospecting. In this paper, we studied on 3D geological modeling, grade estimation and quantitative information extraction, sponsored by Dingjiashan 3D visualized prospecting of Pb-Zn deposits.By analyzing prospecting and production data which are accumulated over the years, and referring to the data format of some famous geological 3D softwares, we presented a data model for geological exploration in this paper, and built a geological database in accordance with the model. Based on the geological database, we have improved the predictive accuracy of 3D model of geological body by combining Datamine and Vulcan. We also got wireframe model and geological block model. On the base of geological block model, we used geostatistics for modeling on the grade of geological block, and then calculate its grade per block unit using Ordinary Kriging method.In order to extract quantitative information on the mineralization, we firstly analyzed the ore body localization law of Dingjiashan, and then built a conceptual model on ore-controlling geological factors. Based on the conceptual model, we obtained the variable on ore-controlling geological factors by analyzing TIN model. Many spatial analyses are used for TIN model analysis:trend shape analysis, shape rolling analysis, distance analysis, slope analysis, angle analysis and so on. In order to quantitative analyze the relationship between ore-controlling geological factors and ore distribution, mineralization index was defined and calculated first, followed by establishing its relationship between ore-controlling geological factors by using non-linear regression method. Finally we extracted the favorable index, which lay the foundation for Dingjiashan 3D visualization-based prospecting of Pb-Zn deposits.