The First Discovery of the Easternmost Jehol Biota from Southeastern Jilin,China

正Objective The Jehol Biota,a world-famous Early Cretaceous fossil Lagerst?tte characterized by the traditional Eosestheria-Ephemeropsis-Lycoptera(EEL)assemblage,has a wide distribution over most parts of northern China,the coastal area of southeastern China,southeastern Mongolia,Transbaikalian area of Russia,Korea and Japan(Shao et al.,2017).The Baishan Basin,situated in     

Mixing of Enriched Lithospheric Mantle-Derived and Crustal Magmas: Evidence from the Habo Cenozoic Porphyry in Western Yunnan

New zircon U–Pb ages, whole-rock geochemistry and zircon Hf isotopes from the Habo porphyry Western Yunnan, China, were determined to provide constraints on the timing of uplift of the Eastern Tibetan Plateau. The intrusive rocks consist of shoshonitic porphyry (syenite porphyry and monzonite porphyry). Zircon laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) U–Pb dating indicates coeval emplacement ages of ~35 Ma. The porphyries have alkaline affinities, enrichment in large ion lithophile elements (LILEs) and light rare earth elements (LREEs) (e.g., Rb, Th, U, Pb), with depletion of high field strength elements (HFSEs) (e.g., Nb, Ti, Ta) and weak Eu anomalies. They display uniform Lu–Hf isotopic compositions with negative zircon εHf(t) values ranging from -3.9 to -0.6. The chemical characteristics of the syenite porphyries indicated that they most likely originated from the lower crust, with mantle-derived material involved in their generation. Geochemically, the monzonite porphyries are similar to the syenite porphyries; however, the lower MgO contents suggest that they were produced by different degrees of partial melting of the same lower crust source. Combined with the geochemical and isotopic data in this paper, imply that the alkali-rich porphyries of the Habo polymetallic deposit were derived from the partial melting of lower crust, enriched by mantle magma, formed in a conversion stage from stress extrusion (a strike-slip shear process) to local stress relaxation (a strike-slip pull-apart process) at the Ailaoshao tectonic zone.     

Comparison of two alluvial aquifers shows the probable role of river sediments on the release of arsenic in the groundwater of district Vehari,Punjab, Pakistan

The study was done to assess the effect of the river Sutlej on arsenic (As) contamination. Sampling was done from the alluvial plain with increasing distance from the river Sutlej in district Vehari and compared with the study done in the proximity of River Sutlej. Sixty (60) groundwater samples mostly from shallow depths were collected and analyzed for As concentrations. Multivariate statistical tools (PCA and CA), saturation index, piper plots and Gibbs diagrams were used to detect evidence about the interrelationship and sources of As and other water quality variables responsible for groundwater contamination. Results revealed that As concentration ranged from below detection limit to 156 µg/L indicating that 50% samples exceeding the WHO guidelines (10 µg/L) and 17% exceeding the Pakistan National Environmental Quality Standards (NEQS) limits (50 µg/L) Sutlej. The piper plot revealed that water chemistry of the study area was Ca–HCO₃^?, Ca–Mg–Cl, type. Correlations between As and HCO₃^? (r²?=?0.433) was positive, while negative correlations were observed between As–Mn²⁺ and As–Fe²⁺ (r²?=???0.102), (r²?=?0.107) respectively. Geochemical signatures of the groundwater in the study area showed that the As could be released by oxidative dissolution to some extent and elevated evaporation in the arid environment of the study area under the stimulus of alkaline water and high pH (range 7.1–8.4). Although the concentrations are exceeding the WHO limit in 50% of the water samples but, are less than the previous study done in Mailsi near River Sutlej. Further, the concentrations decreased as the distance from the River increased which shows the probable role of sediments deposited by the River Sutlej.     

Mechanical behavior and micro-structure of cement kiln dust-stabilized expensive soil

In this study, the effects of cement kiln dust (CKD) on the swelling properties, strength properties, and microstructures of CKD-stabilized expansive soil were investigated. Samples were prepared and stabilized with different CKD content ratios, ranging from 0 to 18% by dry mass. The results obtained show that the maximum swelling pressures decrease exponentially with increases in CKD content. Both the cohesion and unconfined compressive strength (UCS) increase at ratios below 10% CKD and then decrease slightly, above that ratio. CKD can also improve the strength of saturated, expansive soil. There is no visible change of UCS for soil without CKD when cured, while the UCS of a sample with 10% CKD content after curing for 90 days is higher than that after curing for only 1 day. This indicates that CKD can improve the long-term strength of expansive soil. Finally, microstructure analysis reveals that the addition of CKD reduces the montmorillonite content of expansive soil and decreases its swelling properties. The addition of CKD also changes the pore volume distribution, both the size and amount of macro-pores and micro-pores decrease with increase in CKD content. For saturated samples, the size of macro-pores is obviously reduced, while that of micro-pores is slightly increased for both treated and untreated soils. Hydration and saturation processes make the soil structure become dispersive which results in a lower strength, and adding CKD can restrain this process. The suggested optimal CKD content is between 10 and 14% and with a curing time of more than 27 days.     

粤东鸿沟山金矿区流纹斑岩锆石SHRIMP U-Pb定年、Lu-Hf同位素组成及其地质意义

鸿沟山矿区位于华南粤东莲花山钨矿床的中部,是近年来通过地质矿产调查新发现具有找寻金矿潜力的矿床之一。本文以与鸿沟山金等多金属矿化关系密切的流纹斑岩为研究对象,开展了锆石SHRIMP U-Pb定年、流纹斑岩和矿石岩石地球化学、锆石Lu-Hf同位素组成特征研究。获得流纹斑岩锆石206Pb/238U加权平均年龄为169.1±1.5Ma,说明岩体形成于中侏罗世。在微量元素特征方面,流纹斑岩都表现为相对亏损大离子亲石元素Ba、Sr和高强场元素Nb、P、Zr、Hf,富集大离子亲石元素Rb,高强场元素U、Ce、Sm和轻稀土元素La、Nd,流纹斑岩与矿石稀土元素特征基本相似,Eu具中等负异常,但随着矿化强度的增强稀土元素更加富集。流纹斑岩εHf(t)(-15.0~2.2)分布范围宽,二阶段模式年龄TDM2主要变化范围为0.82~0.93 Ga,表明成岩物质主要来源于新元古代古老地壳变质泥岩和变质砂岩部分熔融,并有幔源物质的加入。因此,围绕着该时间段的流纹斑岩岩体,对于找寻W、Cu、Pb、Zn、Bi、Au、稀土、稀有和稀散元素矿产具有重要意义。     

基于高分二号卫星数据的煤矿区地质灾害信息提取研究

随着遥感数据获取技术和能力的全面提高,遥感数据呈现出明显的大数据特征。发展适应于遥感大数据的智能分析和信息挖掘技术,成为当前遥感技术研究的前沿。高分二号（GF-2）卫星数据是我国首颗自主研发的亚米级高分辨率卫星数据,具有观测幅宽、重访周期短、高辐射精度、高定位精度等优势,为未来我国地质灾害的长期、动态地监测和研究提供了高精度、稳定可靠的数据源。本文选取安徽谢桥煤矿2015年1月8日的GF-2卫星影像为研究数据,在对煤矿区主要地质灾害遥感地学分析的基础上,采用面向对象的影像分析方法对研究区由采煤活动所诱发的地质灾害信息进行自动提取。结果表明：利用GF-2卫星数据能够有效地识别地质灾害体的位置、范围、形态等空间分布特征;面向对象的自动提取方法对于煤矿区大面积的积水塌陷盆地、小规模的塌陷坑和线性的地裂缝都有很高的提取精度,识别精度达90% 以上;基于逐层剔除的思路构建的提取规则,为GF-2数据在地质灾害调查和大数据分析中的应用提供了很好的技术支持,也为其它地物目标的提取提供了参考,但在特征的选择和阈值的设定上需要具体分析。     

Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data

With the acceleration of urbanisation in China, preventing and reducing the economic losses and casualties caused by urban rainstorm waterlogging disasters have become a critical and difficult issue that the government is concerned about. As urban storms are sudden, clustered, continuous, and cause huge economic losses, it is difficult to conduct emergency management. Developing a more scientific method for real-time disaster identification will help prevent losses over time. Examining social media big data is a feasible method for obtaining on-site disaster data and carrying out disaster risk assessments. This paper presents a real-time identification method for urban-storm disasters using Weibo data. Taking the June 2016 heavy rainstorm in Nanjing as an example, the obtained Weibo data are divided into eight parts for the training data set and two parts for the testing data set. It then performs text pre-processing using the Jieba segmentation module for word segmentation. Then, the term frequency–inverse document frequency method is used to calculate the feature items weights and extract the features. Hashing algorithms are introduced for processing high-dimensional sparse vector matrices. Finally, the naive Bayes, support vector machine, and random forest text classification algorithms are used to train the model, and a test set sample is introduced for testing the model to select the optimal classification algorithm. The experiments showed that the naive Bayes algorithm had the highest macro-average accuracy.