A layered random forest model-based copper-nickel sulfide ore deposit mineralization prediction method

Abstract

The invention discloses a layered random forest model-based copper-nickel sulfide ore deposit mineralization prediction method, which comprises the following steps of: S1, collecting multivariate geological data in an area, and establishing a copper-nickel sulfide ore deposit geology information database; S2, analyzing the mineralization rule of the copper-nickel sulfide ore deposit in the region,and extracting ore finding information; S3, selecting non-mineral points, constructing a training sample set in combination with known mineral points, and trainin a layered random forest model; S4, optimizing the hierarchical random forest model, and performing mineralization prediction by using an optimization model; and S5, verifying a prediction result, and evaluating the importance of the prospecting information. According to the method, the layered random forest model is used for carrying out mineralization prediction on the copper-nickel sulfide ore deposit, and the problems of ore point misclassification and prediction accuracy reduction caused by imbalance of training samples can be effectively solved, so that the mineralization potential of the copper-nickel sulfide ore deposit in the region is evaluated more objectively and accurately, and a foundation is laid for the next exploration and development work.

Description

technical field [0001] The invention relates to the technical field of evaluating the mineralization potential of copper-nickel sulfide deposits, and more specifically, to a method for predicting the mineralization of copper-nickel sulfide deposits based on a hierarchical random forest model. Background technique [0002] Nickel is an important precious metal with high temperature resistance, oxidation resistance, and corrosion resistance. It is widely used in civil, military, medical and other fields, and has important strategic significance. However, with the decrease of shallow nickel deposits and easily identifiable nickel deposits, the difficulty of nickel deposit exploration becomes more and more difficult. Therefore, it is necessary to use more accurate and efficient mineral prediction methods to improve the efficiency of copper-nickel sulfide deposit exploration. [0003] The use of machine learning models for metallogenic prediction can usually achieve better resul...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the technical problem that the traditional machine learning model cannot use unbalanced sample sets for training in the actual metallogenic prediction process, which affects the accuracy of metallogenic prediction, and proposes a copper-nickel sulfide deposit based on a hierarchical random forest model Metallogenic prediction method

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