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.     

俄罗斯-蒙古地学断面地壳模型的地质-地球物理资料综合研究

地学断面是指地壳的垂直剖面,主要通过对地质和地球物理资料的综合分析来揭示构造带的性质及其空间关系。横断面的研究所采用的数据基本包括100 km宽区域地质图、上地壳的地质剖面图、重磁图(沿横断面的重磁剖面图)以及地壳的地震波速度、密度和其他地球物理属性的剖面图。这些数据被用于构建综合的数据剖面图(结果图),以展示各种地球动力学条件下(裂谷、海洋、碰撞带、造山盆地、大陆地台和岩浆弧,包括安第斯岛弧、活动大陆边缘、海沟、弧前和弧后盆地)的特定的岩石组构。本项目的研究目标是根据研究区现存的地质和地球物理数据的综合解释,统一图例,建立研究区深部剖面,以确定地体的空间关系及其在板块构造方面的地球动力学性质。 前人已分别对东西伯利亚南部和蒙古境内的多个地体进行了构造划分,并对它们的地球动力学性质和时空关系进行了分析。研究结果显示该系列地体为早古生代、中晚古生代和晚古生代—早中生代的岛弧和微大陆。此外,研究还识别出了中—晚古生代和晚古生代—早中生代安第斯型活动大陆边缘、晚古生代—早中生代被动大陆边缘和早白垩世裂谷。与岛弧和安第斯型活动大陆边缘相关的岩体被推覆至相邻大陆和微陆块上,部分推覆宽度可达150 km。目前已开展泥盆纪到晚侏罗世时期蒙古-鄂霍次克海地区的古地球动力学重建。 “非地槽”型花岗岩类岩浆作用在板块构造方面找到了直接且合理的解释,其中泥盆纪—石炭纪和二叠纪—三叠纪岩浆作用区域对应于安第斯型活动大陆边缘,中—晚侏罗世岩浆作用则与西伯利亚/蒙古-中国大陆板块碰撞有关。碰撞岩浆作用中亚碱性(地幔)元素的存在及其所在的构造区域在很大程度可以说明蒙古-鄂霍次克海闭合后,巨厚大陆岩石圈下曾经发生过持续的大洋裂谷活动(地幔热点)。在早白垩世时期,大陆裂谷活动影响到了同一时期正在发生的大陆汇聚作用。 西伯利亚南部边界大部分具有安第斯型活动大陆边缘性质,这也是蒙古—鄂霍次克缝合线沿线蛇绿岩数量较少的原因。因为当汇聚大陆一个具有安第斯类型的活动边缘,而另一个具有被动边缘时,前者的大陆地壳会最终逆冲到后者之上,并因此破坏掉先前出露的蛇绿杂岩体。部分被破坏的蛇绿岩块是俯冲带保留下来的海山残余,其可能成为增生-俯冲楔体的混沌复合体的一部分。然而,由于快速俯冲作用,这种楔形体在晚二叠世—早侏罗世的积累并不是西伯利亚活动边缘的典型特征。 沿地学断面综合的地质和地球物理资料分析表明,亚洲大陆是在显生宙时期由部分前寒武纪微陆块构造拼贴而成的。前寒武纪地块间存在不同宽度的已变形且剥蚀强烈的显生宙火山弧,它们也被归类为特定地体。     

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.     

Resistivity of reservoir sandstones and organic rich shales on the Barents Shelf:Implications for interpreting CSEM data

Marine controlled source electromagnetic(CSEM)data have been utilized in the past decade during petroleum exploration of the Barents Shelf,particularly for de-risking the highly porous sandstone reservoirs of the Upper Triassic to Middle Jurassic Realgrunnen Subgroup.In this contribution we compare the resistivity response from CSEM data to resistivity from wireline logs in both water-and hydrocarbon-bearing wells.We show that there is a very good match between these types of data,particularly when reservoirs are shallow.CSEM data,however,only provide information on the subsurface resistivity.Careful,geology-driven interpretation of CSEM data is required to maximize the impact on exploration success.This is particularly important when quantifying the relative re-sistivity contribution of high-saturation hydrocarbon-bearing sandstone and that of the overlying cap rock.In the presented case the cap rock comprises predominantly organic rich Upper Jurassic-Early Cretaceous shales of the Hekkingen Formation(i.e.a regional source rock).The resistivity response of the reservoir and its cap rock become merged in CSEM data due to the transverse resistance equivalence principle.As a result of this,it is imperative to understand both the relative contributions from reservoir and cap rock,and the geological sig-nificance of any lateral resistivity variation in each of the units.In this contribution,we quantify the resistivity of organic rich mudstone,i.e.source rock,and reservoir sandstones,using 131 exploration boreholes from the Barents Shelf.The highest resistivity(＞10,000 Ωm)is evident in the hydrocarbon-bearing Realgrunnen Subgroup which is reported from 48 boreholes,43 of which are used for this study.Pay zone resistivity is primarily controlled by reservoir quality(i.e.porosity and shale fraction)and fluid phase(i.e.gas,oil and water saturation).In the investigated wells,the shale dominated Hekkingen Formation exhibits enhanced resistivity compared to the background(i.e.the underlying and overlying stratigraphy),though rarely exceeds 20Ωm.Marine mudstones typically show good correlation between measured organic richness and resistivity/sonic velocity log signatures.We conclude that the resistivity contribution to the CSEM response from hydrocarbon-bearing sandstones out-weighs that of the organic rich cap rocks.     

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.     

基于双波段图像的玉米长势自动观测技术研究

基于双波段图像的玉米长势自动观测方法研究     

1986-2016年辽宁省强降水气候特征

利用1986—2016年62个国家气象站5—10月的逐日和逐小时降水观测资料,分析了辽宁省强降水的发生规律,包括强降水发生范围,逐年、逐月、逐旬、逐时变化特征、空间分布特征、雨强分布规律等。结果表明：1）1994年辽宁省强降水频繁、雨强大、降水范围大,为典型强降水年份。2）辽宁省强降水主要集中在7—8月,尤其是7月下旬—8月上旬,占总发生次数的38.3%。3）早晨和下午为短时强降水高发时段,上午及夜里为短时强降水的低发时段。4）辽宁省强降水呈东多西少、南多北少特点,存在3个高值区, 2个低值区。5）辽宁省短时强降水以20—40mm/h为主,日强降水以50—100mm/24h为主。     

A Statistical Study of Magnetic Flux Emergence in Solar Active Regions Prior to Strongest Flares

Using the data on magnetic field maps and continuum intensity for Solar Cycles 23 and 24,we explored 100 active regions (ARs) that produced M5.0 or stronger flares.We focus on the presence/absence of the emergence of magnetic flux in these ARs 2–3 days before the strong flare onset.We found that 29 ARs in the sample emerged monotonically amidst quiet-Sun.A major emergence of a new magnetic flux within a pre-existing AR yielding the formation of a complex flare-productive configuration was observ...