Application Of Nonlinear Geomathematical Geology Models To Minerlal Resources Prospectivity Mapping For Gold Deposits In Southeast Yunnan, China.

Large numbers of mathematic geology models have been applied to mineral resources prediction and evaluation. In general, these models calculate and identify the relationships between mineral indicators and mineral targets with certain mathematical function (linear or non-linear). Therefore, it is important to select a compatible mathematic geology model to predict targets effectively. The built-in complication of geological subject limits the application of conventional mathematical statistics. The non-linear theories and methods show advantages in mineral resources prediction and evaluation. ANN (Aritifical Neural Network), a complex of non-linear system, can help to profoundly understand the non-linear process of mineralization, and then predict mineral resources more accurately. An other non-linear model, SVM (Support Vector Machine) has good adaptability and classification efficiency with limited samples of high dimension data. Although a high predict accuracy can be reached with non-linear modeling techniques, it is still hard to obtain classify rules, which indicate the preferable metallogenic factors from geological information.In this paper, the application of non-linear models to mineral resources prediction was studied with example of gold deposits prediction in Southeast Yunnan. Furthermore, the geochemical data processing method and metallogenic factors selection were studied before and in modeling. The main achievements include: (1) A geochemical anomaly identifying method, r-P model, is studied with multifractal analysis, and applied successfully to geochemical study in Southeast Yunnan. (2) RBFNN model is employed to gold prospectivity mapping in Southeast Yunnan. Experimental results show that the model can quickly obtain the probability of gold deposits. (3) For solving the shortcoming that it is difficult to get classification rules (metallogenic factor knowledge) in non-linear modeling (takes SVM as an example), this paper uses a technique called support vector machine based recursive feature elimination, or SVM-RFE to rank all input features in SVM, then values metallogenic factors in Southeast Yunnan.