西准噶尔谢米斯台山西缘火山岩岩石地球化学特征及其地质意义

为探讨西准噶尔谢米斯台山西缘新发现的中志留世火山岩的岩石属性和地质特征,对其进行岩石地球化学研究。分析结果显示,岩石以富Si、Sr、Ba为特征且具有较高的Mg#(55~75)、Sr/Y(28~59)和Ba/La(17.52~49.88)值,K/Na值0.33~0.66,大离子亲石元素(LILE)相对富集,高场强元素(HFSE)相对亏损,其中Nb、Ta、Ti强烈亏损,具有轻微的负铕异常(δEu=0.84~0.99)。上述特征显示其类似日本中新世Setouchi火山岩带中的赞岐岩。结合其他研究,推测该火山岩可能为受俯冲板片熔体交代的地幔源区部分熔融的产物,它与区内相近时期埃达克岩等的共生组合意味着西准北部在中志留世—早泥盆世曾受到热的洋壳俯冲机制的影响,为进一步认识博什库尔-成吉斯岩浆弧的性质及构造演化提供了新的证据。     

冀东秦家峪中元古界高于庄组锰矿成因: 来自矿物学和地球化学的制约

冀东秦家峪锰矿赋存于中元古界蓟县系高于庄组二段底部含锰岩系内,其成因尚不明确.以秦家峪锰矿ZK58-2钻孔样品为研究对象,通过显微薄片观察、电子探针分析及全岩地球化学分析等方法,探讨了高于庄组锰矿的锰质来源和沉积环境对成矿的贡献.显微薄片观察、电子探针分析表明,原生矿带中含锰矿物主要为菱锰矿、铁镁菱锰矿、钙菱锰矿、锰方...     

大城市边缘区人居环境系统演变规律——以广州市为例

文章以广州市为例,揭示了大城市边缘区人居环境系统的演变规律:首先,人居环境系统演变过程可以划分为初期、中期、后期三个阶段,各阶段在城市化水平、经济发展状况、基础设施建设、社区形态、景观类型、土地利用等方面都呈现出显著的特征;其次,提出了人居环境容量概念,并借助于GIS空间分析方法,发现边缘区人口密度不断地增加,人居环境容量逐步趋于饱和;第三,大城市边缘区人居环境演变呈现出不均衡的周期性变化。在此基础上,借鉴Verhulst逻辑斯蒂方程与复合生态系统的动力学机制,构建了大城市边缘区人居环境演变的阶段性、生命周期与可持续发展模型。揭示大城市边缘区人居环境系统演变的规律,可为大城市边缘区人居环境系统优化调控提供理论依据,充实人居环境科学理论体系。     

APPLICATION OF CLAYS TO REMOVAL OF RED TIDE ORGANISMS Ⅱ.COAGULATION OF DIFFERENT SPECIES OF RED TIDE ORGANISMS WITH MONTMORILLONITE AND EFFECT OF CLAY PRETREATMENT

Study on the coagulation of four species of red tide organisms(Nitzschia pungens,Skeletonema costatum,Prorocentrum minimum and Noctiluca scintillans)with montmorillonite,and the effect ofmontmorillonite pretreatment on the coagulation shows that the capability for montmorillonite to coagu-late with them is in the order:N.pungens>S.costatum>P.minimum>N.scintillans.Thecoagulation is discussed from the aspects of the structure,shape,size,movement,habit,etc.of differ-ent species and the results are explained theoretically.The experimental results also indicate that the treat-ment of montmorillonite with acid can enhance its coagulating capability.This is due to the fact that Al(OH_2)_6~(+3),exchanged from the clay lattice by hydrogen ion H~+,forms hydroxy-aluminum polymers on thesurface of the montmorillonite.The hydroxy-aluminum polymers positively charge and increase the positivecharacteristic of the clay surface,and also serves as a bridge between adjacent surfaces of particles.Thesetwo functions enhance     

The Potential Vertical Distribution of Bigeye Tuna ( Thunnus obesus ) and Its Influence on the Spatial Distribution of CPUEs in the Tropical Atlantic Ocean

Understanding the potential vertical distribution of bigeye tuna(Thunnus obesus) is necessary to understand the catch rate fluctuations and the stock assessment of bigeye tuna. To characterize the potential vertical distribution of this fish while foraging and determine the influences of the distribution on longline efficiency in the tropical Atlantic Ocean, the catch per unit effort(CPUE) data were compiled from the International Commission for the Conservation of Atlantic Tunas and the Argo buoy data were downloaded from the Argo data center. The raw Argo buoy data were processed by data mining methods. The CPUE was standardized by support vector machine before analysis. We assumed the depths with the upper and lower limits of the optimum water temperatures of 15℃ and 9℃ as the preferred swimming depth, while the lower limit of the temperature(12℃) associated with the highest hooking rate as the preferred foraging depth(D12) of bigeye tuna during the daytime in the Atlantic Ocean. The preferred swimming depth and foraging depth range in the daytime were assessed by plotting the isobath based on Argo buoy data. The preferred swimming depth and vertical structure of the water column were identified to investigate the spatial effects on the CPUE by using a generalized additive model(GAM). The empirical cumulative distribution function was used to assess the relationship between the spatial distribution of CPUE and the depth of 12℃ isolines and thermocline. The results indicate that 1) the preferred swimming depth of bigeye tuna in the tropical Atlantic is from 100 m to 400 m and displays spatial variation; 2) the preferred foraging depth of bigeye tuna is between 190 and 300 m and below the thermocline; 3) the number of CPUEs peaks at a relative depth of 30 –50 m(difference between the 12℃ isolines and the lower boundary of the thermocline); and 4) most CPUEs are within the lower depth boundary of the thermocline levels(LDBT) which is from 160 m to 230 m. GAM analysis indicates that the general relationship between the nominal CPUE and LDBT is characterized by a dome shape and peaks at approximately 190 m. The oceanographic features influence the habitat of tropical pelagic fish and fisheries. Argo buoy data can be an important tool to describe the habitat of oceanic fish. Our results provide new insights into how oceanographic features influence the habitat of tropical pelagic fish and fisheries and how fisheries exploit these fish using a new tool(Argo profile data).     

任意各向异性介质三维非结构谱元法直流电阻率正演模拟研究

各向异性是地电异常解释中不可忽视的因素,广泛存在于裂隙或层理发育的地质环境中.本文针对任意各向异性条件下直流电阻率法三维正演问题进行研究,结合非结构谱元法建立模拟算法,充分利用谱方法的指数收敛性以及非结构有限元对地形和复杂异常体刻画能力,提高计算精度和效率.通过灵活的四面体网格剖分和高阶谱插值,实现了复杂介质任意各向异性模型电阻率响应的高精度数值模拟.我们首先通过层状各向异性模型验证本文非结构谱元法的计算精度,进而我们以半空间中立方体模型为例分析各向异性对电阻率响应的影响特征,并通过计算针对不同各向异性参数的视电阻率极性图,探究地下介质各向异性特征识别方法.最后,我们针对典型的山脊模型计算和分析存在地形效应条件下各向异性电流场分布及视电阻率特征.模型计算结果表明基于四面体网格的谱元法模拟带有复杂地形和异常体的任意各向异性模型具有很高的计算精度.本文的研究成果将在推进电阻率方法用于解决裂隙及层理等环境和工程地质问题中发挥积极作用.

     

基于带权Voronoi图与地标的空间位置描述

地标是最基本的空间知识,在空间知识的表达和推理中具有重要作用,常被用来作为空间位置描述的参照物.在实际中,不同地标在空间位置描述中的权重不同,如何考虑地标权重建立符合人类空间认知的形式化方法是空间知识表达的重要研究内容.该文从视觉、语义和空间结构等方面总结影响地标权重的因素,通过认知实验获取北京大学校园内10个地标的权重.在此基础上,基于带权Voronoi图提出生成指定目标对象空间位置描述的形式化方法,并采用认知实验中的数据,以北京大学为例,生成校园内4个指定目标对象的空间位置描述.最后分析了引起基于带权Voronoi图与地标空间位置描述不准确的因素,并提出下一步的研究方向.     

全面禁采以来锡西澄南地区地面沉降动态特征分析

锡西澄南地区在苏锡常地区地面沉降研究中具有一定代表性,是地面沉降的典型地区之一。本文在已有的长期地下水水位、地面沉降监测资料的基础上,结合区域地质条件就全面禁采以来锡西澄南地区的地面沉降特征的诸多方面作进一步的探索分析。分析结果显示:目前锡西澄南地区出现南北沉降差异;差异特征与相应区域的地下水补给条件密切相关;北部Ⅱ承压层沉降为预防重点。     

湖北潜江凹陷古近系深层富钾卤水成因及演化

地下深层富钾卤水是非常重要的钾盐资源,目前很少从区域尺度系统研究沉积盆地中富钾卤水水化学特征及成因.对潜江凹陷深层富钾卤水进行了主、微量元素分析.研究区卤水矿化度为125.70～347.00 g/L,K含量为0.32～6.83 g/L;富集Li、B、I、Na、Cl,亏损Mg、Br,Ca、SO4有富集也有亏损.储层岩石矿...     

The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.