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