Abstract:

Mineralization is a complex physical and chemical process. In the quantitive prediction and evaluation of mineral resources, prediction uncertainties are common in the final predication results due to geologic factors, improper data collection and processing, use of empirical parameters, etc. On the basis of recognizing the sources of uncertainty, uncertainty reduction becomes a major research direction in mineral prospecting. A combined metallogenic prediction method using the forward inverse technique has proven to be an effective solution for reducing uncertainties in the geological anomaly analysis. The combined method is composed of numerical simulation of metallogenic processes based on the genetic model of deposit, and model-driven prediction and evaluation technique based on prospecting model and data. As the combined method takes into account both mineralization processes and model-driven prediction/evaluation in the geo-anomaly analysis, it reduces multiple interpretations of a single metallogenic information therefore reducing prediction uncertainty. In its core contents of analyzing metallogenic patterns and establishing prospecting models, the combined method, mainly using spatial database and geostatistics and GIS analysis techniques as well as 3D geological model building, performs weight of evidence analysis and information value evaluation of the distribution of geological variables to investigate its influence on the prediction result. The aim is to build a “3D cube prediction model” for quantitive prediction, fulfilling the goal of Location, Quantity, Probability quantification in blind orebody prospecting. In conclusion, the combined prediction method, as an important basis for delineating the “5P” prospecting areas, was born by innovatively combining two prediction methods that have complementary predictive functions and values. By doing so, a complete forward inverse combined prediction scheme is realized. We show by example that the combined method can improve accuracy and reduce uncertainties in the quantitative prediction and evaluation of mineral resources, which helps to promote the transformation of geoscience research from qualitative to quantitative.

Key words: geological anomalies, quantitative prediction, uncertainty, combined prediction