Abstract : Recent progresses in geosciences make more and more multidisciplinary data available for mining exploration. This allows developing methodologies to compute predictivity for gold zones by the statistical analysis of variable input parameters. Using newly developed software, the spatial distribution and the topology of polygons (e.g. intrusions) and polylines (e.g. shear zones) are controlled by parameters defined by users (e.g. density, length, surface, etc.). The distance of points of interest (gold deposits) with respect to a given type of objects (polygons or polylines) is given using a probability distribution function. The statistical analyses of output results from the direct modeling process show that i) values of relative surface mean of polygons, relative length mean of polylines, the number of objects and their clustering are critical to statistical appraisals, ii) the validity of the different tested inversion methods strongly depends on the relative importance and on the dependency between the parameters used, and iii) the robustness of the inferred distribution points of interest laws with respect to the quality of the input data. This approach was applied to the geological and geophysical data of the Yenissei ridge of the total area of 75730 km2 for the predictivity mapping of 29 new gold zones with the total area of 1811 km2. The newly developed method allows reducing up to four times of the area of predictivity gold zones, compared with previous studies. For more accurate construction of gold zones, a 3D density model of the Yenisei ridge was constructed. This model is based on surface gravity and aeromagnetic data (numerical grids of 1x1km), ―Batholite‖ and ―Shpate‖ seismic and magnetotelluric profiles, respectively. The 3D density model shows that: a) the Yenissei ridge has a cover-folded structure, formed during a Neopretorozoic collisional event, b) only γNPta Tatarsky-Ayhta granites and shear zones have spatial relationships with gold mineralization.