Copper mineralization pattern and machine learning-based copper prospectivity prediction in Laos
Laos is located in the southeastern segment of the Tethyan metallogenic domain,in the southern extension of the Sanjiang metallogenic belt.It has abundant mineral resources but is lacking high-level geological research.Metallogenic and mineral prospectivity modeling,therefore,is an effective way to achieving major breakthroughs in mineral exploration in Laos.The 1∶1000000 national-scale geochemical mapping project in Laos has provided high-quality geochemical baseline data and maps for mineral resource and environmental evaluation.This paper utilizes data obtained from the mapping project,combined with the metallogenic pattern of known minerals in Laos,and applies machine learning techniques to predict propective copper resource areas.The results show that(1)the formation of copper deposits in Laos is significantly controlled by tectonic-magmatic-sedimentary processes.The main types of copper deposits are porphyry,skarn,hydrothermal,and sandstone.(2)The copper content in stream sediments of Laos ranged between 1.20-459 μg/g,with an average value of 21.96 μg/g and a median value of 16.50 μg/g.Among the seven tertiary tectonic units,the average copper content was higher in the Changshan block and three suture zones than in other tectonic units.Geochemical maps reveal uneven distribution of copper,with occurrence of several large,high background and anomaly areas.(3)A quantitative,multisource information prediction model for copper deposits in Laos was constructed,with model factors such as single-element anomalies,multielement combination anomalies,multielement combinations indicative of acidic rocks,the distribution of ore-controlling structures,and the distribution of carbonate and clastic rocks.(4)Using the Random Forest metallogenic prediction method,nine metallogenic prospective areas were delineated,which have great prospecting potential for various types of copper deposits,such as porphyry and skarn.
prospecting area predictionmachine learningcopper mineralization patternsgeochemical mappingLaos
万方数据
维普
