| 1 | Petrogenetic contrastive studies on the Mesozoic early stage ore-bearing and late stage ore-barren granites from the southern Anhui Province显示文摘Yanshanian magmatisms are intensive in the southern Anhui Province and can be divided into early(152–137 Ma)and late(136–122 Ma) stages. A Yanshanian granitic zone was found to crop out along Qingshan to Changgai areas in the Tunxi district in Field investigation which has a genetic link with molybdenum multiple metal mineralization. To be a representative syenitic granite in the southern Anhui Province, the Huangshan pluton has not been found so far to have any genetic link with mineralization. Zircon LA-ICP-MS dating indicate that the four granitic bodies from the Qingshan-Changgai zone have concurrent formed ages from 140±4 to 141±2 Ma, belonging to the Yanshanian early stage magmatism. However, the Huangshan granite is dated to be 129±2 Ma, belonging to the Yanshanian late stage magmatism. The Qingshan-Changgai granites show high SiO2 and K2 O contents, low P2O5 contents and middle Al2O3 contents and are high-K calc-alkaline series metaluminum I-type granite. These rocks are characterized by enrichments in the large ion lithophile elements and light rare earth elements(REE), depletions in the high field-strength elements, and middle degree negative anomalies of Eu, geochemical features of arc or continent crustal derived magma affinities. These rocks have 87Sr/86Sr(t) ratios from 0.7120 to 0.7125, εNd(t) values from.7.24 to.4.38 and zircon εHf(t) values of.4.4 to 6.7, similar to that of the coeval ore-bearing granodiorites in the southern Anhui Province. Integrated geochemical studies indicate that the Yanshanian ore-bearing granodiorites were formed by partial melting of the Meso-Neoproterozoic accreted thickened low crust. Meanwhile, the Qingshan-Changgai granites were formed through a AFC process of plagioclase+amphibole+Shangxi Group of magmas that formed the ore-bearing granodiorites. The Huangshan granites are characterized by high SiO2 and K2 O contents, moderate Al2O3 contents, seagull shape REE distributed pattern and distinct Eu negative abnormities. Comparing with the Qingshan-Changgai granites, the Huangshan granites show more Ba, Sr, P,and Ti negative abnormities with no Nb and Ta depletions and are high-K calc-alkaline series metaluminum A-type granite. εHf(t)values of the Huangshan granites are from.6.6 to.1.2, similar to that of the early stage ore-bearing granodiorites, indicating that they were also formed by anatexis of the Meso-Neoproterozoic accreted crust, but their magma sources might be residual granulitic crust which ever underwent Yanshanian early stage I-type intermediate-acid magma extraction. Comparing studies on the two stages granites indicate that the early stage granites derived from a relative thickened low crust under a lower temperature condition. Their magma sources were Meso-Neoproterozoic accreted crust which enriched in ore-forming materials and further became more enriched through processes of magma AFC evolution. However, the late stage A-type granites originated from relative shallow crust under a higher temperature condition. Their magma source was depleted in ore-forming materials due to the early stage magma extraction and thus had weak ore-forming capacity. From early to late stage, the magmatisms tectonic setting translated from post-orogenic to anorogenic and the later corresponded to a back-arc extensional setting as increase of the slab subducted angle of the Paleo-Pacific plate. | YAN Jun HOU TianJie WANG AiGuo WANG DeEn ZHANG DingYuan WENG WangFei LIU JianMin LIU XiaoQiang LI QuanZhong | 2017 | Science China Earth Sciences2017，60，11： | 9 |
| 2 | Estimation of Crop Biomass Using GF-3 Polarization SAR Data Based on Genetic Algorithm Feature Selection显示文摘In recent years,Polarization SAR(PolSAR)has been widely used in the filed of crop biomass estimation.However,high dimensional features extracted from PolSAR data will lead to information redundancy which will result in low accuracy and poor transfer ability of the estimation model.Aiming at this problem,we proposed a estimation method of crop biomass based on automatic feature selection method using genetic algorithm(GA).Firstly,the backscattering coefficient,the polarization parameters and texture features were extracted from PolSAR data.Then,these features were automatically pre-selected by GA to obtain the optimal feature subset.Finally,based on this subset,a support vector regression machine(SVR)model was applied to estimate crop biomass.The proposed method was validated using the GaoFen-3(GF-3)QPSΙ(C-band,quad-polarization)SAR data.Based on wheat and rape biomass samples acquired from a synchronous field measurement campaign,the proposed method achieve relative high validation accuracy(over 80%)in both crop types.For further analyzing the improvement of proposed method,validation accuracies of biomass estimation models based on several different feature selection methods were compared.Compared with feature selection based on linear correlation,GA method has increased by 5.77%in wheat biomass estimation and 11.84%in rape biomass estimation.Compared with the method of recursive feature elimination(RFE)selection,the proposed method has improved crops biomass estimation accuracy by 3.90%and 5.21%,respectively. | Kunpeng XU Lei ZHAO Kun LI Erxue CHEN Wangfei ZHANG Hao YANG | 2020 | Journal of Geodesy and Geoinformation Science2020，3，4： | 3 |
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