Sapphirine–quartz granulites from the Cocachacra region of the Arequipa Massif in southern Peru record early Neoproterozoic ultrahigh‐temperature metamorphism. Phase equilibrium modelling and zircon petrochronology are used to quantify timing and pressure–temperature (P–T) conditions of metamorphism. Modelling of three magnetite‐bearing sapphirine–quartz samples indicates peak temperatures of >950°C at ~0.7 GPa and a clockwise P–T evolution. Elevated concentrations of Al in orthopyroxene are also consistent with ultrahigh‐temperature conditions. Neoblastic zircon records ages of c. 1.0–0.9 Ga that are interpreted to record protracted ultrahigh‐temperature metamorphism. Th/U ratios of zircon of up to 100 reflect U‐depleted whole‐rock compositions. Concentrations of heavy rare earth elements in zircon do not show systematic trends with U–Pb age but do correlate with variable whole‐rock compositions. Very large positive Ce anomalies in zircon from two samples probably relate to strongly oxidizing conditions during neoblastic zircon crystallization. Low concentrations of Ti‐in‐zircon (<10 ppm) are interpreted to result from reduced titania activities due to the strongly oxidized nature of the granulites and the sequestration of titanium‐rich minerals away from the reaction volume. Whole‐rock compositions and oxidation state have a strong influence on the trace element composition of metamorphic zircon, which has implications for interpreting the geological significance of ages retrieved from zircon in oxidized metamorphic rocks. 相似文献
This study used spatial autoregression（SAR） model and geographically weighted regression（GWR） model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity. Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased. SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices（i.e.,farmland density and temporal change）. The GWR model has smooth process when constructing the farmland spatial model. The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations. The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious. The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors. 相似文献
A model integrating geo-information and self-organizing map (SOM) for exploring the database of soil environmental surveys was established. The dataset of 5 heavy metals (As, Cd, Cr, Hg, and Pb) was built by the regular grid sampling in Hechi, Guangxi Zhuang Autonomous Region in southern China. Auxiliary datasets were collected throughout the study area to help interpret the potential causes of pollution. The main findings are as follows: (1) Soil samples of 5 elements exhibited strong variation and high skewness. High pollution risk existed in the case study area, especially Hg and Cd. (2) As and Pb had a similar topo-logical distribution pattern, meaning they behaved similarly in the soil environment. Cr had behaviours in soil different from those of the other 4 elements. (3) From the U-matrix of SOM networks, 3 levels of SEQ were identified, and 11 high risk areas of soil heavy metal-contaminated were found throughout the study area, which were basically near rivers, factories, and ore zones. (4) The variations of contamination index (CI) followed the trend of construction land (1.353) > forestland (1.267) > cropland (1.175) > grassland (1.056), which suggest that decision makers should focus more on the problem of soil pollution surrounding industrial and mining enterprises and farmland.
