Geology and Mineral Chemistry of Uranium- and Thorium-bearing Minerals in Rare-Metal (NYF) Pegmatites of Um Solimate,South Eastern Desert,Egypt

The garnet muscovite granitic pegmatite of Um Solimate, in southern Egypt, represents a promising asset for strategic and economic metals, especially Bi–Ni–Ag–Nb–Ta as well as U and Th. The ore bodies occur as large masses, pockets and/or veins of very coarse-grained pegmatites, which consist mainly of K-feldspar, quartz and albite with subordinate muscovite, garnet, and biotite. Radiometric data revealed that eU- and eTh-contents of the pegmatites reach up to 39 ppm and 82 ppm, respectively. The studied pegmatites are enriched in primary U and Th minerals (uraninite, coffinite, thorianite and uranothorite) as well as Hf-rich zircon and monazite, which give rise to anomalous radioactive zones. Niobium-tantalium-bearing minerals (i.e. ferrocolumbite, microlite and uranopyrochlore), xenotime, barite, galena, fluorite, and apatite are ubiquitous, and, consequently, the studied pegmatites belong tothe Niobium–Yttrium–Fluorine-type (NYF) family. The noble metal mineralization includes argentite (Ag2S), native Ni and Bi as well as bismite and bismoclite. In addition, beryl and tourmaline are observed in pegmatites near the contact with metasediments and ultramafic bodies. The observed compositional variations of Ta/(Ta+Nb) and Mn/(Mn+Fe) ratios in columbite (0.08–0.45 and 0.11–0.57, respectively) and Hf contents in zircon (3.54–6.46 wt%) may reflectan extreme degree of magmatic fractionation leading to formation of the pegmatite orebody.     

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.     

Minimal cut-based recursive decomposition algorithm for seismic reliability evaluation of lifeline networks

In this paper, a new probabilistic analytical approach, the minimal cut-based recursive decomposition algorithm (MCRDA), is presented to evaluate the seismic reliability of large-scale lifeline systems. Based on the minimal cut searching algorithm, the approach calculates the disjoint minimal cuts one by one using the basic procedure of the recursive decomposition method. At the same time, the process obtains the disjoint minimal paths of the system. In order to improve the computation efficiency, probabilistic inequality is used to calculate a solution that satisfies the prescribed error bound. A series of case studies show that MCRDA converges rapidly when the edges of the systems have low reliabilities. Therefore, the approach can be used to evaluate large-scale lifeline systems subjected to strong seismic wave excitation.     

2007年宁洱6.4级地震余震序列尾波QC值研究

基于Aki和Chouet的地方震尾波单次散射模型,利用思茅台记录到的2007年云南宁洱6.4地震余震的数字化波形观测资料,测量了震源区尾波Qc(f)值。当中心频率为1.5Hz时,宁洱地区的尾波Qc值在51～147之间,平均值为79,尾波的振幅衰减率βc(f)在0.014～0.039之间,平均值为0.028;测量得到该地区尾波Qc值与频率f的关系为Qc(f)=53f0.88;尾波波源因子与震级成正比关系,满足关系lgA0=1.51ML-0.94;测量结果显示宁洱地震余震序列的尾波值Qc较低,表明宁洱震源区属于构造运动较为活跃区。     

New observations and transit solutions of the inflated exoplanets HAT-P-40b and HAT-P-51b

We present high-precision photometric observations of the transiting exoplanets HAT-P-40 b and HAT-P-51 b by the Rozhen 2-m telescope.The newly-observed transit of HAT-P-40 b is the first one with a complete curve.The orbital periods of the two targets were improved.We modeled the observed transits and found bigger stellar radii than those derived from the stellar models.The planet radii of HATP-40 b and HAT-P-51 b obtained from our transit solutions are bigger than the values calculated by the empirical relations for Jupiter-mass and Saturn-mass planets respectively.Their values reveal the highlyinflated nature of the two targets,especially that of HAT-P-51 b.We established that the best transit solutions correspond to a quadratic limb-darkening law.The fitted limb-darkening coefficients of HAT-P-40 are close to the theoretical ones while those of HAT-P-51 are a little different.The precise astrometric Gaia distances of the two targets are smaller by 6%–7%than the calculated values from the stellar models.We propose the Gaia distances to be used for improvement of the stellar models as well as for more reliable calculation of the parameters of the known exoplanets.     

气候影响下的荒漠下的荒漠化监测

根据揭示沙漠化扩张内在规律的相关气候驱动因子的假说,气候作用影响之下的沙漠化是在具反射率-降水正、负反馈特点的气候系统之中的区域干旱化过程与干旱土壤退化过程相互作用的结果。在辐射调节下的地表下垫面温度占主导的条件下,气候成因的沙漠化是由区域气候系统反馈所形成。正反馈的外部调节因子是降水量的变化和极端降水事件(特别是旱灾)的出现频率,降水量减少以及降水频率的降低直接影响沙漠化的程度。20世纪下半叶,人类因素导致的干旱土地退化作为另外的正反馈调节因子,其重要性正在增加。通常正反馈的扩大趋势会被通过蒸腾而进行的地表温度调节的负反馈所抑制。由正反馈到负反馈的转换是通过地球表面与大气层之间的热量交换的调节所确定的,其中非潜热因子处于增加状态。上述情况在反射率和地表温度同时增加的条件下发生。在超出绿色植物生物量及其NDVI指数阈值的条件下,负反馈向正反馈的转换就会发生。人为作用导致的植被退化加快了向绿色植物生物量阈值逼近的进程,从而引起气候因素驱动的沙漠化扩大。一个主要由气候因素影响的荒漠化区域,在大多数情况下,其绿色生物量均在季节变化和年际变化中达到了阈值。在中度的人为影响的植被退化条件下,这些地域一般都包括部分干旱与半干旱土地。然而有时,人类活动也会导致干旱的半湿润土地退化。本文运用Turan和Sahel地区1982-2001年NDVI指数的监测结果,分析了全球变暖和人为作用导致的土地退化。     

多元统计分析在金川铜镍矿床两类矿石中的应用

应用聚类分析和因子分析对金川铜镍硫化物矿床样品及其微量元素进行了分析和科学分类,并对分类的样品进行R型因子分析,找寻在成矿作用过程中对成矿贡献起关键性作用的因子。因子分析结果表明,富矿类型的网状矿石与贫矿类型的星点状矿石的Ni,Cu矿化期存在着明显的差异性;网状矿石在成矿作用过程中,经受了晚期晚阶段的中、低温热液作用,使矿石中的主要成矿元素Cu产生了进一步的叠加和富集,并伴有Bi,Se,Te及贵金属元素的富集。因子分析的研究为脉动式多期多阶段的成矿机制模式提供了新的依据,同时,也为矿床中有用伴生元素的赋存状态及找寻提供了有用的成因信息。     

Geochemistry and Petrogenesis of Late Ediacaran Rare-metalAlbite Granites of the Arabian-Nubian Shield

The Abu Dabbab albite granite(ADAG), in the central Eastern Desert of Egypt, hosts the most significant rare metal ore deposit in the northern part of the Neoproterozoic Arabian-Nubian Shield. Here, we report detailed field,petrographic, mineralogical and geochemical investigation of the ADAG, an isolated stock-like granitic body with sharp intrusive contacts against metamorphic country rocks, probably emplaced at about 600 Ma. The fine-grained porphyritic upper unit is a preserved remnant of the shallowly-emplaced apex of the magma chamber, whereas the medium-grained lower unit crystallized at deeper levels under subvolcanic conditions. The peraluminous leucocratic ADAG shares common geochemical characteristics with post-collisional intraplate A-type magmas. In addition to the conspicuous enrichment in Na2 O, the ADAG is remarkable for its anomalous concentrations of Ta, Nb, Li, Hf, Ga, Sn, Zn and heavy rare-earth elements. Nb-Ta minerals in the ADAG are mixed with Fe-Mn oxides, forming black patches that increase in abundance toward of the base of the intrusion. Columbite-tantalite, cassiterite and wolframite are the most important ore minerals.Pronounced negative Eu anomalies(Eu/Eu* = 0.10–0.24) reflect extreme magmatic fractionation and perhaps the effects of late fluid-rock interaction. The ADAG was most likely generated by partial melting of the juvenile middle crust of the ANS as the geotherm was elevated by erosional uplift following lithospheric delamination and it was emplaced at the intersection of lineations of structural weakness. Although formation of the ADAG and its primary enrichment in rare metals are essentially due to magmatic processes, late-stage metasomatism caused limited redistribution of rare metals. Fluid-driven subsolidus modification was limited to the apex of the magma chamber and drove development of greisen, amazonite, and quartz veins along fracture systems.     

Benthic infaunal communities around two artificial reefs in Mamala Bay, Oahu, Hawaii

Non-fishery use of artificial reefs has been given attention in recent years. The primary concern associated with non-fishery artificial reefs is their effects to the surrounding ecosystems. This study examined the infaunal communities around two non-fishery artificial reefs (the sunken vessels YO257 and Sea Tiger) in Mamala Bay, Hawaii. Infaunal community structures at these artificial reefs were relatively similar to one at a nearly natural patch reef. A large amount of basalt gravel around YO257 associated with its deployment operation possibly had an effect on the surrounding community by increasing pore space. Polychaete assemblages were compared with existing data throughout the bay, and this revealed that the variation in sediment grain sizes and depths seemed to play some role in structuring the polychaete communities. Nevertheless, the infaunal communities around the artificial reefs were typical and within the range of natural variation in Mamala Bay, supporting their beneficial uses in ecotourism.