Palaeoenvironmental Reconstruction of the Early to Middle Miocene Sequence in West Central Sinai,Egypt, as Revealed from Fossil Diatoms

A detailed analysis of the diatoms from the sedimentary sequence exposed in Abu Qada basin, west central Sinai, was used to determine the palaeoenvironmental changes during the Lower to Middle Miocene. A total of 85 diatom species and varieties belonging to 37 genera were identified from 154 samples collected throughout the stratigraphic succession. The lithological characters of the studied samples varied between sandstone, silty interbeds, sandy shales, shales, and terminated with anhydrite and limestones. These rock units are included in two lithostratigraphic formations (Rudies and Kareem), which are separated by a marked unconformity. The distribution and preservation of fossil diatoms in the sedimentary record are examined with the aim of outlining the temporal and spatial variation in the composition of the diatom assemblages, in order to estimate the changes in depositional environments during the Lower to Middle Miocene. The distributional pattern of the recorded diatom taxa distinguished four diatom eco-zones. The environment of each eco-zone is deduced and a proposed paleobathymetric change and depositional history of the Miocene sediments in the studied area are given.     

Is Ziarat a Potential Site for Conventional or Unconventional Energy Resources?

A structural interpretation of the Ziarat block in the Balochistan region (a part of the Suleiman Fold and Thrust Belt) has been carried out using seismic and seismological data. Seismic data consists of nine 2.5D pre‐stack migrated seismic lines, whereas the seismological data covers the Fault Plane Solution and source parameters. Structural interpretation describes two broad fault sets of fore and back thrusts in the study area that have resulted in the development of pop‐up structures, accountable for the structural traps and seismicity pattern in terms of seismic hazard. Seismic interpretation includes time and depth contour maps of the Dungan Formation and Ranikot group, while seismological interpretation includes Fault Plane Solution, that is correlated with a geological and structural map of the area for the interpretation of the nature of the subsurface faults. Principal stresses are also estimated for the Ranikot group and Dungan Formation. In order to calculate anisotropic elastic properties, the parameters of the rock strength of the formations are first determined from seismic data, along with the dominant stresses (vertical, minimum horizontal, and maximum horizontal). The differential ratio of the maximum and minimum horizontal stresses is obtained to indicate optimal zones for hydraulic fracturing, and to assess the potential for geothermal energy reservoir prospect generation. The stress maps indicate high values towards the deeper part of the horizon, and low towards the shallower part, attributed to the lithological and structural variation in the area. Outcomes of structural interpretation indicate a good correlation of structure and tectonics from both seismological and seismic methods.     

Satellite estimates and subpixel variability of rainfall in a semi-arid grassland

卫星估雨精度的不确定性受到当地降雨类型和像元内降雨非均匀性影响,而结合这两个关键因素开展半干旱草原卫星估雨的研究有限.2009年夏,我们在中国锡林郭勒半干旱草原用多部微雨雷达和雨量计构建了9 km卫星像元降雨观测网,观测了像元内降雨非均匀性(空间变异系数CV),并评估了卫星估雨精度.结果表明:(1)CV值受像元内平均降雨量,降雨类型,降雨云面积及移向等影响,如高Cv值的降雨过程大多为平均降雨量小,对流性降雨过程,降雨云边缘像元CV值较高;(2)TRMM 3B42V7卫星估雨产品适用性较好,CMORPH和PERSIANN次之,但TRMM 3B42V7易在半干旱草原湖泊处高估降雨.     

Intraseasonal oscillation of the southwest monsoon over Sri Lanka and evaluation of its subseasonal forecast skill

斯里兰卡的雨季发生于5-9月间,主要受西南季风的控制.本文发现该地区的西南季风降水存在很强的次季节变率,主导周期为10-35天.降水的季节内变化与西传的异常气旋有关.进一步,利用S2S比较计划中欧洲中心的数值预报模式(ECMWF)提供的回报试验数据,评估了当今动力模式对斯里兰卡西南季风次季节变化的预报技巧.结果显示,对季风指数的预测技巧超过30天,而对降水指数的预测技巧大约两周,且模式的预报技巧具有明显的年际差异.分析表明,能否正确模拟出大尺度环流对热带对流的响应是影响斯里兰卡降水预测的重要因子.     

间接DGM(1,1)模型在基坑沉降预测中的应用

选取某一基坑沉降监测点,该点11个周期的累计沉降量为近似非齐次指数增长序列,以Java为工具对该点进行编程计算,得到GM(1,1)、DGM(1,1)、间接DGM(1,1)3种模型的基坑沉降预测结果。对比分析发现,间接DGM(1,1)模型精度高于GM(1,1)和DGM(1,1)模型,其C值仅为0.01,且残差值增加缓慢,近似于一条水平线,实测值与预测值非常接近,适用范围广,弥补了另两种模型不能进行长期预测的缺憾。     

新疆卡鲁安硬岩型锂矿床成矿流体性质：来自He—Ar同位素证据

卡鲁安锂矿床位于新疆北部的阿尔泰造山带,是以锂辉石为主要矿石矿物的硬岩型锂矿床。前人对该矿床的岩石成因、成矿机制及构造背景已经有了初步的认识,但对该矿区内成矿流体的研究仍是空白,这将在一定程度上影响对矿床成因的认识。本文通过分析卡鲁安伟晶岩中锂辉石和石英流体包裹体He、Ar同位素组成,对成矿流体进行示踪研究。研究表明,含矿伟晶岩的n(3He) /n(4He) 为0. 25~3.19 Ra（平均0.97 Ra）,无矿伟晶岩与外围伟晶岩n(3He) /n(4He) 为0.13~5.32 Ra（平均1.13 Ra）,均介于壳源与幔源He之间。根据成矿流体的壳幔二元混合模式进行计算：含矿伟晶岩中的地幔流体比例为3.55% ~ 48.92%,平均值为14.67%;无矿伟晶岩与外围伟晶岩地幔流体占比为1.70% ~ 81.79%,平均值为17.13%。含矿伟晶岩成矿流体的n(40Ar) /n(36Ar)为552.50~13353. 00,n(40Ar*)相对含量为46.52% ~ 97.79%,平均值为87.25%,大气的Ar贡献平均为12.75%。分析结果显示,成矿流体主要以壳源流体为主,部分幔源流体和改造型饱和大气水的混合流体,随着成矿作用的进行,地幔He与大气饱和水改造Ar皆有所减少。值得注意的是卡鲁安锂矿床成矿流体中幔源物质并非真的来自于地幔物质上侵,更有可能是来源于元古代的不成熟陆壳熔融。新疆卡鲁安锂矿床形成于陆—陆碰撞造山作用晚期的后碰撞造山阶段,造山后期的伸展导致含幔源物质的古老地壳与年轻地壳减压熔融,熔融所形成的岩浆流体随后经大气降水改造为成矿流体。     

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.