Abstract: A method for determining a direction and a distance of a metallogenic pluton of a skarn deposit with a garnet includes: collecting a sample; performing petrographic observation on the sample and designing an experimental area; computing a parameter; building a model; setting a parameter range; optimizing the parameters of the three models through a grid search method, and determining an optimal model and corresponding parameters a?, b and c through repeated circulative iteration and by taking a minimum R as a limit condition; and substituting data of all sampling points into the optimal model, computing D of each sampling point, drawing a circle with a corresponding sampling point as a center and D as the radius, determining an intersection point of all circles as coordinates of the metallogenic pluton, and performing delineation with R as a buffer area of the metallogenic pluton.

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.

Abstract: A GPU-based integrated processing method for geochemical data, includes the following steps: S1, storing geochemical data in a format of a preset three-dimensional array data structure Struct_A, S2, iteratively processing the formatted geochemical data by using a compute shader in a GPU, calculating a mean value and a standard deviation of content of each chemical element, calculating a threshold value of the chemical element, eliminating sampling points according to the threshold value, repeating the operations until there is no sampling point to be eliminated, and calculating a final mean value as a background value and a final threshold value as a lower limit value of anomaly; and S3, constructing spatial coordinates according to the obtained new geochemical data set, loading the spatial coordinates into a fragment shader for rendering, and completing anomaly delineation according to the background value and the lower limit value of anomaly.

Abstract: A method for determining a direction and a distance of a metallogenic pluton of a skarn deposit with a garnet includes: collecting a sample; performing petrographic observation on the sample and designing an experimental area; computing a parameter; building a model; setting a parameter range; optimizing the parameters of the three models through a grid search method, and determining an optimal model and corresponding parameters a?, b and c through repeated circulative iteration and by taking a minimum R as a limit condition; and substituting data of all sampling points into the optimal model, computing D of each sampling point, drawing a circle with a corresponding sampling point as a center and D as the radius, determining an intersection point of all circles as coordinates of the metallogenic pluton, and performing delineation with R as a buffer area of the metallogenic pluton.