Gold Mine in Muji Area of Xinjiang Based on Random Forest Algorithm-Regional Metallogenic Prediction
The Pamir tectonic junction in the western section of the Indian-Eurasian continental collision orogenic belt is located in the muji-Wuzibieli mountain pass.It is found that there are many gold-copper deposits(points),and a large number of gold deposits are developed,showing great gold prospecting potential.With the deepening of geological pros-pecting work in the area,a large amount of geological prospecting information has been accumulated.Mature prediction theory and method are needed to obtain the distribution location,output probability and resource potential of mineral re-sources in the area,so as to achieve efficient metallogenic prediction.On the basis of summarizing the geological charac-teristics of the gold mining area in Muji area,the regional Au element anomaly is analyzed,and various remote sensing alteration anomalies in the area are extracted.The geological-geochemical-remote sensing comprehensive prediction model is constructed,and the quantitative prediction of multi-information integration is carried out by using the random forest algorithm.Based on the random forest algorithm to quantitatively predict the probability of prospecting,combined with the current research status of the study area and the type,quantity,representativeness and regularity of known ore deposits,the prospecting target areas are optimized,and three A-level prospecting target areas are delineated.Two B-level prospecting target areas and one C-level prospecting target area.The machine learning based on random forest algorithm has better prediction accuracy and improves the efficiency of prediction in the multi-data area of geophysical and geo-chemical remote sensing,which provides a basis for efficient quantitative prediction in this area.
NETL
NSTL
万方数据
