Applications of Radial Basis Functional Link Networks in the Exploration for Lala Copper Deposits in Sichuan Province, China.

The Lala copper area in Huili County, Sichuan Province, China, is favored by superior regional metallogenic geological conditions due to its location in an extremely important copper–iron metallogenic belt in southwest China, and it has witnessed the formation of a series of unique iron–copper deposits following the superposition of multiple tectonic events. In recent years, major mineral exploration breakthroughs have been achieved in the deep and peripheral zones of this area. Using the Lala copper mining area in Sichuan as an example, this paper describes metallogenic prediction research carried out based on multivariate geoscience information (geological information, geophysics, geochemistry, and remote sensing data) and the application of geographic information system (GIS) technology and the radial basis function neural network (RBFLN) model. The five specific aspects covered in this paper are as follows: (1) we collected geology–geophysics–geochemistry remote sensing data and other information, adopted GIS technology to extract multivariate geoscience ore-forming anomaly information, and established a geoscience prospecting information database; (2) we applied the RBFLN algorithm for information on integrated analysis of ore-forming anomalies in the study area; (3) we applied a statistical method to divide the threshold value to delineate favorable ore-prospecting target areas; (4) we applied three-dimensional (3D) visualization technology, through which sample assistance was verified, to evaluate the performance of the RBFLN model; and (5) the results revealed that the RBFLN model can integrate multivariate and multi-type geoscience information and effectively predict metallogenic prospective areas and delineate favorable target areas. The metallogenic prediction method based on RBFLN technology provides a scientific basis for the exploration and deployment of minerals in the study area. It is obvious that the methods to predict and evaluate mineral resources are developing towards model integration and information intelligent analysis. [ABSTRACT FROM AUTHOR]