Analysis of Characteristics and Mineralization Process of Polymetallic Ore Deposit in South China

Abstract

Based on the analysis of the characteristics and mineralization process of the polymetallic ore deposit in South China, this paper applies the improved nonlinear technology (GA-SVM) to the metallogenic prognosis, and provides a new idea for the prediction of the favorable degree of mineralization. On the basis of the analysis of the South China ore district mineralization favorable degree, select 28 learning samples and 10 geological variables related to ore-forming. Support vector machine (SVM) method based on genetic algorithm(GA) is applied for the construction of the mineralization favorable model, and compare with the prediction results of BP neural network model. The results show that the GA-SVM regression model can well predict the nonlinear relationship between the favorable degree and the geological variables. When the number of samples are limited, GA-SVM has higher fitting precision than BP neural network, more suitable for nonlinear metallogenic prediction work. Metallogenic favorability analysis helps to understand the process of polymetallic deposit, with strong promotion significance.