Prediction of ultrabasic rocks by support vector machine based on airborne magnetic and radioactivity data

Abstract

Copper-nickel (Cu-Ni) deposits are short-supply strategic mineral resources. Ultrabasic rocks are the important ore-forming geological bodies of Cu-Ni deposits. The accurate identification of ultrabasic rocks is important for predicting Cu-Ni deposits. With the high-level prospecting, the outcropped ultrabasic rocks are mostly discovered, while the concealed ones are difficult to detect by geological mapping. Geophysical data contains deep information that is important in concealed ultrabasic rock prospecting. Based on the physical properties (high magnetic susceptibility and low radioactivity), we use large-range, high-precision airborne magnetic and radioactive data to predict undiscovered ultrabasic rocks. With the development of artificial intelligence technology, support vector machines (SVM) have the advantage of strong classification ability and are suitable for small samples. Because of the influence of several parameters, it is difficult to get a prediction result with high geological interpretability. Therefore, genetic algorithms (GA) with global optimization are used in this study to obtain better SVM parameters. The geological factors are first proposed to describe ultrabasic rock characteristics and added into the fitness functions of GA to improve accuracy and geological interpretability. The geological factors consist of shape (Fshape), area (Farea), and scatter (Fsactter). Consequently, support vector machines optimized by genetic algorithms (GASVM) are applied to predict ultrabasic rocks in the Qilian orogen. The airborne magnetic and radioactive data consider different geophysical properties and reveal geological characteristics of ultrabasic rocks from deep to shallow. The results are better than the prediction by a single dataset. In addition, we test three standardization methods, the rising ridge distribution standardization method could effectively and efficiently improve the prediction accuracy. The perdition accuracies are higher than 85% compared with the known ultrabasic rocks. The prediction ranges are concentrated in less than 10% of the whole area. Two ultrabasic rocks are first predicted in this study which is confirmed by field investigation. Meanwhile, concealed ultrabasic rocks are predicted in the southeast of the study area, which are not shown on the geological map. The results can provide general and reliable distributions of ultrabasic rock and rapidly delineate the favorite area for Cu-Ni deposit prospecting.