A Feature-Enhanced MPS Approach to Reconstruct 3D Deposit Models Using 2D Geological Cross Sections: A Case Study in the Luodang Cu Deposit, Southwestern China

Three-dimensional (3D) models of orebodies and deposits can provide strong support for quantitative evaluation of mineral resources. However, 3D modeling of ore deposits based on limited mineral exploration data remains a challenge due to the non-stationarity of the spatial distribution of orebodies and the uncertainty of data and models. Stochastic methods based on multiple-point geostatistics (MPS) have shown promise in automatic 3D characterization of complex geological structures (e.g., minerals, rocks, facies). However, as an essential element of MPS methods, a credible training image is difficult to obtain in real 3D applications. The spatial distribution of orebodies is critically non-stationary because the formation and evolution of a deposit are complex and its shape and distribution are controlled by faults, surrounding rock and other structures. In this work, we propose a feature-enhanced MPS-based approach, namely FE-3DRCS, by combining the characteristics of geological exploration data distribution following a MPS method called 3DRCS. To reduce the influence of non-stationarity of orebodies, a spatial stationarity enhancement strategy is presented. Besides, the artifact elimination strategy of spatial features and the adaptive optimization strategy based on an iterative mechanism are integrated in our approach. By performing FE-3DRCS on the geological exploration data in the Luodang Cu deposit, southwestern China, a set of 3D deposit models and Cu grade models were built. A series of morphological comparisons and statistical analyses were followed to evaluate the spatial distribution characteristics of orebodies. The Cu reserve was also evaluated based on the built 3D grade models. The real application confirms that FE-3DRCS can be applied effectively in the fields of 3D characterization and quantitative evaluation of mineral resources.