长江中下游成矿带及邻区Moho深度与成矿背景探讨

长江中下游成矿带是我国最主要的铜铁金多金属成矿带之一。本文利用卫星重力数据计算布格重力异常,采用波数域迭代的Parker-Oldenburg位场迭代反演方法,通过重力数据反演获得长江中下游成矿带及邻区的三维Moho面结构,结合研究区已有的研究成果,探讨了研究区的深部构造格局。研究结果表明:研究区范围内Moho面总体从南西往北东有逐渐变浅趋势,最浅处位于长江口海域和杭州湾海域约28~29km,最深处位于大别山区约38~39km;长江中下游及邻区下方Moho面的隆起形态呈"V"字型,与地表的"V"字型构造特征相呼应,Moho面隆起的最深处位于"V"字型转折端附近,向两侧Moho面呈抬升状,北东走向的"V"字型东支隆起幅度和规模都要明显大于北西走向的"V"字型西支;卫星化极磁异常显示长江中下游成矿带的正磁异常高值集中分布在"V"字型东支的宁芜、庐枞、繁昌、怀宁等火山岩盆地;钦杭成矿带东段Moho面也明显上隆,隆起的中心沿钦杭成矿带的南侧边界断裂的东南侧线性分布。长江中下游成矿带和钦杭成矿带东段的Moho面条带状隆起带可能指示了地幔上隆、岩浆上涌并在壳幔边界处与下地壳发生底侵作用的深部边界,来自于壳、幔的深部含矿岩浆通过深大断裂等地壳薄弱点向上运移,并在适当的位置形成金属矿集区。     

区域创新网络模式研究——以长三角城市群为例

网络范式的兴起赋予城市创新模式新的内涵,引起了经济地理学者对不同空间尺度知识流动和创新联系的关注。基于网络视角,以中国知识产权局2014年长三角城市群26个地级市合作发明专利信息为原始数据,借助Ucinet、ArcGIS等分析工具,从本地和跨界多维空间尺度,刻画长三角城市群创新网络结构,测度城市创新网络地位,评价城市创新能力,进而对城市创新模式进行划分。研究表明：①研发密集的大型国有企业、中外合资企业和知名的理工科院校具有较高的知识生产能力,成为长三角城市群创新合作优先链接主体;②长三角城市群重视外部知识获取,跨界网络成为重要的创新合作途径,地理距离对创新合作空间载体选择的制约减弱;③创新网络位置影响知识获取和城市创新,网络视角下的长三角城市群呈现四类创新模式,密集的“本地—跨界”创新网络有助于城市创新。研究结论对长三角城市群不同类型创新模式的优化升级具有一定的参考价值。     

Is the age problem resolved?

The cosmological, astrophysical, and nucleocosmochronological methods for estimating the age of the universe and the corresponding uncertainties are comparatively studied in the present paper. We are led to the conclusion that the new measurements of cosmological parameters, and the recent estimates of the age of globular clusters have led to the gradual disappearance of the age problem from the arena of modern cosmology.     

渤东凹陷东南斜坡构造变换带识别及其石油地质意义

根据渤东凹陷斜坡带新钻井以及新采集的三维地震等勘探资料,采用构造样式解剖、断裂分析以及油气成藏规律分析等方法研究构造变换带特征及其石油地质意义。结果表明,渤东凹陷东南斜坡带上存在构造变换带,此变换带具有调节局部构造变形和分割构造带的作用,把斜坡带分割为西、中、东3个整体呈NE向的似花状构造带。变换带对于明下段极浅水三角洲沉积体展布具有一定控制作用,是砂体形成岩性尖灭的有利场所,同时,近SN向变换断层对NEE向断裂的封堵易形成构造圈闭、构造—岩性圈闭,具有较大的勘探潜力。3个似花状构造带的南部3条断层的断面与地层呈顺向配置,形成断面汇聚脊到顺向地层的优势运移路径,同时受到南部呈雁行排列的密集断裂带的阻挡,形成了研究区一条似花状构造内部的优势运聚范围,最终在渤东凹陷东南斜坡带上形成密集断裂带横向遮挡的主控断层—地层产状耦合油气富集模式。     

Geomorphological analysis of the morphometric characteristics that determine the volume of sediment yield of Wadi Al-Arja,South Jordan

This study addresses the morphometric variables that determine the sediment yield in Wadi Al-Arja through the analysis of the impact of different morphometric characteristics along the course of the valley on its sediment yield, as well as the analysis of spatial and formal dimensions and morphologies of the basin and its relationship to the sediment yield. The study also addresses the size of variation in the volume of sediment yield of the river tributaries that make up the water network of the valley under the differences of its morphometric and hydrological characteristics. The study found several results, most notably: The classification of Wadi Al-Arja basin according to the hypsometric integral value (72.1%) within an uneven topography, which increases the force of the erosive activity and the size of the sediment yield if the basin was exposed to moist climatic periods. The study also showed the presence of convergence in the intra-spaces between the river tributaries in the water network. This increases the volume of water flows when these tributaries meet with each other and thus increasing their erosive ability and sediment yield.The study also showed the presence of marked variation in the sediment yield of the river tributaries depending on the differences in its morphometric characteristics. The results of the step-wise regression analysis confirmed the importance of the morphometric and hydrological variables, and plant coverage in interpreting the variation in the size of the sediment yield of the river tributaries of different stream order in Wadi Al-Arja basin, where these variables interpreted 43% of the total variation, with statistical significance less than 0.05.     

2D seismic reflection contribution to structural and geometric study of Cenozoic aquifer systems in the Sisseb El-Alem basin,central-eastern Tunisia

The Sisseb-ElAlem basin in central-eastern Tunisia contains several aquifers intensely used in agriculture. Highly exploited shallow aquifers are vulnerable to pollution and drought and reveal high salinity. Consequently, the new hydrogeological surveys were oriented to deep aquifers. The present work corresponds to study of 2D seismic sections, calibrated by water and petroleum wells, and geological outcrops. It highlights geometry and faulting of buried and preserved structures in depth and leads to identify the spatial distribution of Cenozoic potential reservoir layers in the different paleogeographic domains. The Sisseb-ElAlem basin is affected mainly by near E- and N-trending faults. Iso-depth and iso-thickness mapping were used to follow lateral and vertical variations of sedimentary series related to tectonics. The study area consists of Sisseb, Sbikha and El Ktifa uplifts, and El Alem, and Bled Saadia subsiding synclines. The Oligocene-Miocene reservoir series are exploited locally at the high borders. They reveal thickening and deepening southward in the El Alem-Bled Saadia domain, where they can be exploited at about 400 to 2000 m depths.This study should contribute to the orientation of water research to deeper aquifers, and therefore, ensure sufficient drinking and irrigation water supply mainly in areas called for sustainable development.     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Impact of drilling mud on chemistry and microbiology of an Upper Triassic groundwater after drilling and testing an exploration well for aquifer thermal energy storage in Berlin (Germany)

After completion of an exploration well, sandstones of the Exter Formation were hydraulically tested to determine the hydraulic properties and to evaluate chemical and microbial processes caused by drilling and water production. The aim was to determine the suitability of the formation as a reservoir for aquifer thermal energy storage. The tests revealed a hydraulic conductivity of 1–2 E-5 m/s of the reservoir, resulting in a productivity index of 0.6–1 m³/h/bar. A hydraulic connection of the Exter Formation to the overlaying, artesian “Rupelbasissand” cannot be excluded. Water samples were collected for chemical and microbiological analyses. The water was similarly composed as sea water with a maximum salinity of 24.9 g/L, dominated by NaCl (15.6 g/L Cl and 7.8 g/L Na). Until the end of the tests, the water was affected by drilling mud as indicated by the high pH (8.9) and high bicarbonate concentration (359 mg/L) that both resulted from the impact of sodium carbonate (Na₂CO₃) additives. The high amount of dissolved organic matter (>?58 mg/L) and its molecular-weight distribution pattern indicated that residues of cellulose, an ingredient of the drilling mud, were still present at the end of the tests. Clear evidence of this contamination gave the measured uranine that was added as a tracer into the drilling mud. During fluid production, the microbial community structure and abundance changed and correlated with the content of drilling mud. Eight taxa of sulfate-reducing bacteria, key organisms in processes like bio-corrosion and bio-clogging, were identified. It can be assumed that their activity will be affected during usage of the reservoir.     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.