3d design software free download deep exploration

The RF prediction indicates that there is no significant potential in the FHS mine down to depth of 1400 m. Based on the results of 3D geometric and geodynamic modeling, we conducted the random forest (RF) ML model in the FHS mine and the ML models respectively of artificial neural network (ANN), support vector machine (SVM) and RF in the AQ mine to predict mineral potentials. The dynamic simulations of the intrusions’ cooling processes suggest that the dilatant deformation produced by the coupled mechano-thermo-hydrological (MTH) processes is favor for mineralization. The 3D variation of resistivity can provide some ambiguous evidences for inferring contact zone and orebodies.

The 3D geological models of the FHS and AQ orefields show that the topography and attitudes of the intrusions’ contact zones have spatial constraints on orebodies. This paper presents our studies on its application in the Fenghuangshan (FHS) and Anqing (AQ) mines, its benefits and pitfalls and the strategies for applying benefits and avoiding pitfalls.

The 3D computational modeling-based machine learning (ML) prediction is an innovative methodology for exploration-targeting.

3d design software free download deep exploration