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 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 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.