Abstract: A method for rapid prediction of porphyry copper mineralization potential based on spectral characteristics of tourmaline, includes collecting and collating data related to magma-fluid evolution, petrography and mineralogy of a porphyry copper deposit in a working area, systematically, distinguishing magmatic tourmaline from hydrothermal tourmaline in the working area, and further distinguishing phases and generations of the hydrothermal tourmaline; collecting hydrothermal tourmaline samples of the same phase and generation; performing short-wave infrared spectroscopy measurement on the collected hydrothermal tourmaline samples; extracting spectral characteristics of the Fe—OH wavelength and Mg—OH wavelength of the tourmaline samples based on The Spectral Geologist (TSG); and when the Fe—OH wavelength is less than 2245.75 nm, and the Mg—OH wavelength is greater than 2356.

Abstract: A method for metallogenic prediction by using multi-source heterogeneous information, includes the following steps: collecting geological, geochemical and remote sensing multi-source geoscience information data; building a conceptual model of a metallogenic system; extracting geoscience multi-source spatial proxy mineralization indication information according to the conceptual model of the metallogenic system; integrating and training data based on a neural network to obtain multi-dimensional spatial proxy layer data sets and training points; inputting the multi-dimensional spatial proxy layer data sets and the training points, and applying a machine learning algorithm for hyper-parameter optimization to obtain an optimized machine learning model; and applying the optimized machine learning model to complete machine learning result evaluation and target area delineation.