Permeable Reactive Barrier for the treatment of contaminated surface water in Katedan industrial development area, Hyderabad, India

Permeable Reactive Barrier （PRB） is an emplacement of inert material （s） in the subsurface, designed to intercept a contaminated plume, provides a preferential flow path through the reactive media, and transforms the contaminant into environmentally acceptable forms to attain concentration remediation goals at the discharge of the barrier. The phenomena, which help in remediation within PRB, are adsorption/sorption, precipitation, oxidation/reduction and biodegradation. Various materials like zero-valent iron, zero-valent bi-metals, natural zeolites, organic carbon, fly ash, zeolites, limestone, activated alumina, apatites, etc. have been tried by many researchers to remove organic and inorganic contaminants. In USA, Canada, and many European countries commercial full-scale and pilot scale PRBs are successfully working. The design and installation of full scale PRBs needs laboratory treatability and dynamic flow column experiments？ The concept of PRB is being applied to treat contaminated surface water in the Katedan industrial area, Hyderabad, India. National Geophysical Research Institute （NGRI）, Hyderabad, India, conducted systematic studies in collaboration with Norwegian Geotechnical Institute （NGI）, Norway, to develop PRB technique to decontaminate the surface water pollution due to industrial effluent. A site assessment study in the Katedan Industrial Area, were carried out and water, soil and sediment from the lakes of the area were found to be polluted with high concentrations of heavy metals like As, Pb, Cr, Cd, Ni, etc. Adsorption studies at NGRI with synthetic samples and in-situ industrial effluent using different reactive media for removing contaminants like arsenic, chromium, cadmium, copper, nickel, lead and zinc have been carried out and yielded satisfactory results. The performance of zero-valent iron and limestone is encouraging in removing As,     

关于科拉片岩带含金条带状铁建造的说明

引言地盾区太古宙绿岩带中某些条带状铁建造的含金特征,已广为人知。科拉尔片岩带是南印度地盾的“古老绿岩带”,含有大量的条带状铁建造。条带状铁建造岩层广布于科拉尔片岩带内,在成分上是均一的。另方面,沿片岩带西边缘产出的条带状铁建造层,在走向上很稳定,延伸数公里。条带状铁建造层是以硫化物相为特征的。早期地质工作者已指出这些硫化物相铁建造的含金特征。本文的目的是介绍科拉尔片岩带不同部     

地铁喷射注浆系统（MJS）

采用高压喷射注浆改善土层的传统方法,由于其排除泥浆的机理,有时引起工地附近的地表隆升或收缩。本文介绍的技术,可在旋喷切割土层后测量和控制浆液的静压力,从而避免对工地附近的地表产生不利影响。此外,该技术施工可用于施工不同方向的钻孔（垂直、水平或倾斜孔）。     

黑龙江嘉荫地区白垩纪-第三纪界线初步观察

本文报道了黑龙江嘉荫白山头地区一条新的地层剖面及其K／T界线的初步研究。新命名的“白山头段”代表乌云组下部的早古新世地层,含以Tiliaephyllum tsagajanicum等为代表的达宁早期植物化石。在白山头剖面上,作者发现可能为白垩纪末期的凝灰岩。结合与俄罗斯布列亚盆地含K／T界线的查加扬群标准剖面的对比,推测嘉荫地区的K／T界线可能位于白山头段底部与上述凝灰岩之上的一层炭质泥岩之间。     

重力数据的加权欧拉反褶积

欧拉反褶积被用来对重磁数据进行快速解释，在圈定接触带和进行深度快速计算方面特别有用。深度计算的精度则大多取决于合适的构造指数的选择，构造指数是引起异常的地质体几何形态的函数。对于重力调查而言观测站的分布一般不规则，并且重力场会受到假频信号的干扰，这就会导致深度计算上的误差。     

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.     

Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers

Soil swelling-related disaster is considered as one of the most devastating geo-hazards in modern history.Hence,proper determination of a soil’s ability to expand is very vital for achieving a secure and safe ground for infrastructures.Accordingly,this study has provided a novel and intelligent approach that enables an improved estimation of swelling by using kernelised machines(Bayesian linear regression(BLR)&bayes point machine(BPM)support vector machine(SVM)and deep-support vector machine(D-SVM));(multiple linear regressor(REG),logistic regressor(LR)and artificial neural network(ANN)),tree-based algorithms such as decision forest(RDF)&boosted trees(BDT).Also,and for the first time,meta-heuristic classifiers incorporating the techniques of voting(VE)and stacking(SE)were utilised.Different independent scenarios of explanatory features’combination that influence soil behaviour in swelling were investigated.Preliminary results indicated BLR as possessing the highest amount of deviation from the predictor variable(the actual swell-strain).REG and BLR performed slightly better than ANN while the meta-heuristic learners(VE and SE)produced the best overall performance(greatest R2 value of 0.94 and RMSE of 0.06%exhibited by VE).CEC,plasticity index and moisture content were the features considered to have the highest level of importance.Kernelized binary classifiers(SVM,D-SVM and BPM)gave better accuracy(average accuracy and recall rate of 0.93 and 0.60)compared to ANN,LR and RDF.Sensitivity-driven diagnostic test indicated that the meta-heuristic models’best performance occurred when ML training was conducted using k-fold validation technique.Finally,it is recommended that the concepts developed herein be deployed during the preliminary phases of a geotechnical or geological site characterisation by using the best performing meta-heuristic models via their background coding resource.