3D Prospectivity Modeling For Concealed Orebody And System Development

With the rapid development of modern industry, mineral resources have become one of the important guarantee for sustained, stable and rapid economic development. However, mineral exploration is becoming increasingly difficult over time, with the majority of easy to discover (i.e., near-surface, in non-remote locations and with easily identifiable geochemical, geological or geophysical footprints) mineral deposits having already been discovered. This has led to an increased focus on targeting of concealed mineral resources both in greenfield and brownfield environments in most areas of China, with a coincident increase in prospectivity modeling and attempts to identify areas under cover that may contain significant mineralization. However, only a few or weak anomalies which reflect the deep concealed ore bodies are present at the surface. So it brings great challenges for prospectivity modeling which are based on the anomaly theory. The development of 3D geological information technology provides a new method for mineral exploration. Therefore combining three-dimensional geological information technology with prospectivity modeling methods to establish a workflow and software&system has very important significance for the future mineral exploration.In order to meet the needs of future exploration, this paper establishes a workflow which includes a lot of 3D spatial analysis methods, geophysical methods and prospectivity models for 3D prospectivity modeling. And based on the workflow and methods, this paper develops a suite of software to help the prospectivity modeling in 3D more efficiently and conveniently. By using the workflow and software above, this paper takes Zhonggu ore field within Ningwu Basin, Middle and Lower Yangtze Metallogenic Belt as an example, to create 3D prospectivity models in deposit-sacle and orefield-scale respectively. Meanwhile, this paper also analyses and compares the predictive efficiency and validity between different prospectivity methods in 3D. The comparison results show that for the prospectivity modeling in 3D, the predictive efficiency of the result by using continuous data is significantly better than the result by using boolean data; and logistic regression method and artificial network method can better summarize the mineral exploration criteria and have more predictive efficiency due to their non-linear characteristics.Based on the prediction result, this paper also highlights a number of new exploration targets in the depth of the zhonggu orefield which can provide quantitative support for the mineral further exploration. Among them, some new targets are around the known deposit, which means depth and periphery of the known deposits should be considered high priority targets for future mineral exploration. In addition, the case study also validates the workflow and the methods, proves they can be worked for 3D prospectivity modeling in deposit scale and ore-field scale very well. It is also proven that the software has good practicality and applicability, can further facilitate the application of 3D prospectivity modeling in mineral exploration for the deep concealed orebodies.