Spinel assemblages in lunar meteorites Graves Nunataks 06157 and Dhofar 1528: Implications for impact melting and equilibration in the Moon's upper mantle

Magnesium‐rich spinel assemblages occur in the two lunar vitric breccia meteorites—Dhofar (Dho) 1528 and Graves Nunataks (GRA) 06157. Dho 1528 contains up to ~0.7 mm cumulate Mg‐rich spinel crystals associated with Mg‐rich olivine, Mg‐ and Al‐rich pyroxene, plagioclase, and rare cordierite. Using thermodynamic calculations of these mineral assemblages, we constrain equilibration depths and discuss an origin of these lithologies in the upper mantle of the Moon. In contrast, small, 10 to 20 μm spinel phenocryst assemblages in glassy melt rock clasts in Dho 1528 and GRA 06157 formed from the impact melting of Mg‐rich rocks. Some of these spinel phenocrysts match compositional constraints for spinel associated with “pink spinel anorthosites” inferred from remote sensing data. However, such spinel phenocrysts in meteorites and Apollo samples are typically associated with significant amounts of olivine ± pyroxene that exceed the compositional constraints for pink spinel anorthosites. We conclude that the remotely sensed “pink spinel anorthosites” have not been observed in the collections of lunar rocks. Moreover, we discuss impact‐excavation scenarios for the spinel‐bearing assemblages in Dhofar 1528 and compare the bulk rock composition of Dho 1528 to strikingly similar compositions of Luna 20 samples that contain ejecta from the Crisium impact basin.     

Experimental partitioning of trace elements into schreibersite with applications to IIG iron meteorites

Some of the defining characteristics of the IIG iron meteorite group are their high bulk P contents and massive, coarse schreibersite, which have been calculated to make up roughly 11–14 wt% of each specimen. In this study, we produced two data sets to investigate the formation of schreibersites in IIG irons: measurements of trace elements in the IIG iron meteorite Twannberg and experimental determinations of trace element partitioning into schreibersite. The schreibersite‐bearing experiments were conducted with schreibersite in equilibrium with a P‐rich melt and with bulk Ni contents ranging from 0 to 40 wt%. The partitioning behavior for the 20 elements measured in this study did not vary with Ni content. Comparison of the Twannberg measurements with the experimental results required a correction factor to account for the fact that the experiments were conducted in a simplified system that did not contain a solid metal phase. Previously determined solid metal/P‐rich melt partition coefficients were applied to infer schreibersite/solid metal partitioning behavior from the experiments, and once this correction was applied, the two data sets showed broad similarities between the schreibersite/solid metal distribution of elements. However, there were also differences noted, in particular between the Ni and P contents of the solid metal relative to the schreibersite inferred from the experiments compared to that measured in the Twannberg sample. These differences support previous interpretations that subsolidus schreibersite evolution has strongly influenced the Ni and P content now present in the solid metal phase of IIG irons. Quantitative attempts to match the IIG solid metal composition to that of late‐stage IIAB irons through subsolidus schreibersite growth were not successful, but qualitatively, this study corroborates the striking similarities between the IIAB and IIG groups, which are highly suggestive of a possible genetic link between the groups as has been previously proposed.     

Variations in glacier volume and snow cover and their impact on lake storage in the Paiku Co Basin,in the Central Himalayas

Glaciers and snow cover are important constituents of the surface of the Tibetan Plateau. The responses of these phenomena to global environmental changes are sensitive, rapid and intensive due to the high altitudes and arid cold climate of the Tibetan Plateau. Based on multisource remote sensing data, including Landsat images, MOD10A2 snow product, ICESat, Cryosat-2 altimetry data and long-term ground climate observations, we analysed the dynamic changes of glaciers, snow melting and lake in the Paiku Co basin using extraction methods for glaciers and lake, the degree-day model and the ice and lake volume method. The interaction among the climate, ice-snow and the hydrological elements in Paiku Co is revealed. From 2000 to 2018, the basin tended to be drier, and rainfall decreased at a rate of −3.07 mm/a. The seasonal temperature difference in the basin increased, the maximum temperature increased at a rate of 0.02°C/a and the minimum temperature decreased at a rate of −0.06°C/a, which accelerated the melting from glaciers and snow at rates of 0.55 × 10⁷ m³/a and 0.29 × 10⁷ m³/a, respectively. The rate of contribution to the lake from rainfall, snow and glacier melted water was 55.6, 27.7 and 16.7%, respectively. In the past 18 years, the warmer and drier climate has caused the lake to shrink. The water level of the lake continued to decline at a rate of −0.02 m/a, and the lake water volume decreased by 4.85 × 10⁸ m³ at a rate of −0.27 × 10⁸ m³/a from 2000 to 2018. This evaluation is important for understanding how the snow and ice melting in the central Himalayas affect the regional water cycle.     

Retrieving volume FeO and TiO_2 abundances of lunar regolith with CE-2 CELMS data using BPNN method

The volume FeO and TiO_2 abundances(FTAs) of lunar regolith can be more important for understanding the geological evolution of the Moon compared to the optical and gamma-ray results. In this paper, the volume FTAs are retrieved with microwave sounder(CELMS) data from the Chang'E-2 satellite using the back propagation neural network(BPNN) method. Firstly, a three-layered BPNN network with five-dimensional input is constructed by taking nonlinearity into account. Then, the brightness temperature(TB) and surface slope are set as the inputs and the volume FTAs are set as the outputs of the BPNN network.Thereafter, the BPNN network is trained with the corresponding parameters collected from Apollo, Luna,and Surveyor missions. Finally, the volume FTAs are retrieved with the trained BPNN network using the four-channel TBderived from the CELMS data and the surface slope estimated from Lunar Orbiter Laser Altimeter(LOLA) data. The rationality of the retrieved FTAs is verified by comparing with the Clementine UV-VIS results and Lunar Prospector(LP) GRS results. The retrieved volume FTAs enable us to re-evaluate the geological features of the lunar surface. Several important results are as follows. Firstly, very-low-Ti(1.5 wt.%) basalts are the most spatially abundant, and the surfaces with TiO_2 5 wt.% constitute less than 10% of the maria. Also, two linear relationships occur between the FeO abundance(FA) and the TiO_2 abundance before and after the threshold, 16 wt.% for FA. Secondly, a new perspective on mare volcanism is derived with the volume FTAs in several important mare basins, although this conclusion should be verified with more sources of data. Thirdly, FTAs in the lunar regolith change with depth to the uppermost surface,and the change is complex over the lunar surface. Finally, the distribution of volume FTAs hints that the highlands crust is probably homogeneous, at least in terms of the microwave thermophysical parameters.     

Subsurface flow measurements using passive flux meters in variably-saturated cold-regions landscapes

To date, passive flux meters have predominantly been applied in temperate environments for tracking the movement of contaminants in groundwater. This study applies these instruments to reduce uncertainty in (typically instantaneous) flux measurements made in a low-gradient, wetland dominated, discontinuous permafrost environment. This method supports improved estimation of unsaturated and over-winter subsurface flows which are very difficult to quantify using hydraulic gradient-based approaches. Improved subsurface flow estimates can play a key role in understanding the water budget of this landscape.     

Phase Shift between Changes in Global Temperature and Atmospheric CO2 Content under External Emissions of Greenhouse Gases into the Atmosphere

Izvestiya, Atmospheric and Oceanic Physics - The phase shift between changes in the global surface temperature Tg and atmospheric CO2 content $${{q}_{{{\text{C}}{{{\text{O}}}_{2}}}}}$$ has been...     

Integration of Electrofacies and Hydraulic Flow Units to Delineate Reservoir Quality in Uncored Reservoirs: A Case Study,Nubia Sandstone Reservoir,Gulf of Suez,Egypt

Natural Resources Research - Recognition of reservoir quality is an important objective in reservoir characterization process. By definition, the quality of a reservoir is described by its...     

毛乌素沙区1961-2016年降水特征

基于毛乌素沙区10个气象站1961-2016年观测资料，应用Mann-Kendall方法和t检验法对各气象站年降水量进行了突变检验，借助小波分析讨论了各气象站年降水量的周期特征，根据降水量等值线划分结果对整个研究区分区分析了年、季、月和日尺度上的降水变化特征，并在两个时段上分析了季节性降水的差异。结果表明：毛乌素沙区年降水量空间特征差异明显，东部亚区呈上升趋势，中西部亚区呈下降趋势，但变化趋势不显著且无突变发生；降水年内分配不均，干湿季分明，降水集中在5-9月，夏秋季降水占全年降水比重大，季、月和日尺度降水量存在梯度递减变化；年降水量的年际变化过程存在多重时间尺度的自相似结构；近26年的冬春季降水增加显著，但降水波动幅度小于前30年。     

Classification of stellar spectra with SVM based on within-class scatter and between-class scatter

Support Vector Machine (SVM) is a popular data mining technique, and it has been widely applied in astronomical tasks, especially in stellar spectra classification. Since SVM doesn’t take the data distribution into consideration, and therefore, its classification efficiencies can’t be greatly improved. Meanwhile, SVM ignores the internal information of the training dataset, such as the within-class structure and between-class structure. In view of this, we propose a new classification algorithm-SVM based on Within-Class Scatter and Between-Class Scatter (WBS-SVM) in this paper. WBS-SVM tries to find an optimal hyperplane to separate two classes. The difference is that it incorporates minimum within-class scatter and maximum between-class scatter in Linear Discriminant Analysis (LDA) into SVM. These two scatters represent the distributions of the training dataset, and the optimization of WBS-SVM ensures the samples in the same class are as close as possible and the samples in different classes are as far as possible. Experiments on the K-, F-, G-type stellar spectra from Sloan Digital Sky Survey (SDSS), Data Release 8 show that our proposed WBS-SVM can greatly improve the classification accuracies.