Numerous intermediate to felsic igneous rocks are present in both subduction and collisional orogens.However,porphyry copper deposits(PCDs)are comparatively rare.The underlying factors that differentiate fertile magma...Numerous intermediate to felsic igneous rocks are present in both subduction and collisional orogens.However,porphyry copper deposits(PCDs)are comparatively rare.The underlying factors that differentiate fertile magmas,which give rise to PCDs,from barren magmas in a specific geological setting are not well understood.In this study,three supervised machine learning algorithms:random forest(RF),logistic regression(LR)and support vector machine(SVM)were employed to classify metallogenic fertility in southeastern Tibet,Sanjiang orogenic belt,based on whole-rock trace element and Sr-Nd isotopic ratios.The performance of the RF model is better than LR and SVM models.Feature importance analysis of the models reveals that the concentration of Y,Eu,and Th,along with Sr-Nd isotope compositions are crucial variables in distinguishing fertile and barren samples.However,when the optimized models were applied to predict the datasets of Miocene Gangdese porphyry copper belt and Jurassic Gangdese arc representing collision and subduction settings respectively,a marked decline in metrics occurred in all three models,particularly on the subduction dataset.This substantial decrease indicates the compositional characteristics of intrusions across different tectonic settings could be diverse in a multidimensional space,highlighting the complex interplay of geological factors influencing PCD’s formation.展开更多
基金financially supported by the National Key Research and Development Program of China(2019YFA0708602,2022YFF0800903)National Natural Science Foundation of China(42472112,U2244217,41973045)+1 种基金Basic Science and Technology Research Fundings of the Institute of Geology,CAGS(JKYZD202312)Geological Survey Projects of the China Geological Survey(DD20242878,DD20243512).
文摘Numerous intermediate to felsic igneous rocks are present in both subduction and collisional orogens.However,porphyry copper deposits(PCDs)are comparatively rare.The underlying factors that differentiate fertile magmas,which give rise to PCDs,from barren magmas in a specific geological setting are not well understood.In this study,three supervised machine learning algorithms:random forest(RF),logistic regression(LR)and support vector machine(SVM)were employed to classify metallogenic fertility in southeastern Tibet,Sanjiang orogenic belt,based on whole-rock trace element and Sr-Nd isotopic ratios.The performance of the RF model is better than LR and SVM models.Feature importance analysis of the models reveals that the concentration of Y,Eu,and Th,along with Sr-Nd isotope compositions are crucial variables in distinguishing fertile and barren samples.However,when the optimized models were applied to predict the datasets of Miocene Gangdese porphyry copper belt and Jurassic Gangdese arc representing collision and subduction settings respectively,a marked decline in metrics occurred in all three models,particularly on the subduction dataset.This substantial decrease indicates the compositional characteristics of intrusions across different tectonic settings could be diverse in a multidimensional space,highlighting the complex interplay of geological factors influencing PCD’s formation.
文摘单晶Si C硬度高、脆性大,加工困难,在塑性域加工时处于纳米尺度才可明显改善表面质量、获得高的精度。而单晶Si C的切削机理研究使用有限元和实验方法,无法获得时间尺度在飞秒或皮秒下材料发生的变化。为此,采用分子动力学模拟方法,对单晶3C-Si C切削过程进行了建模和仿真,分析了在不同切削速度、切削深度下切削力的变化。研究结果表明:切削速度为50 m/s、100 m/s和200 m/s时对应的平均切向切削力为737.34 n N、635.29 n N和587.09 n N,单晶Si C表面采用合适的切削速度能减小切削过程的切削力。
