鄂尔多斯盆地中央古隆起的地质结构与成因机制

克拉通盆地内古隆起是了解板内变形机制的重要窗口,也是油气、煤、盐、铀等矿产勘查的主要对象,一直是盆地分析的热点领域。本文以鄂尔多斯盆地中央古隆起为例,利用新的高精度反射地震剖面与深探井资料,解剖古隆起的地质结构,刻画构造演化过程,探讨其形成机制。研究表明,鄂尔多斯盆地古生代隆起带发育在华北克拉通地块中西部,自伊盟—杭锦旗、乌审旗—定边,南延至镇原—正宁一带,呈“工”字形,面积达7.5×104 km2以上;伊盟凸起为基底隆起,镇原隆起为多阶段、间隙性发育的剥蚀与沉积型隆起,定边凸起为沉积型隆起。它是基底分异构造格局与周缘板块构造事件共同控制的产物,为在中元古代裂陷基础上早古生代被动大陆边缘背景下发育演化的古隆起;古生代早期,邻接周缘洋盆表现为台缘隆起;石炭纪晚期—二叠纪早期,因差异升降而具陆缘隆起性质;印支晚期—燕山期以来,隐伏于天环坳陷之下埋藏沉没。鄂尔多斯盆地中央隆起带为多机制复合成因,伊盟凸起为基底强烈伸展、长期剥露剥蚀型隆起;镇原凸起为西缘断阶迁移、南缘前缘隆起叠加的复合隆起;定边凸起为断阶迁移、挤压叠加的继承性隆起。鄂尔多斯盆地中央隆起带对盆地内部的构造—沉积分异施加了重要影响,其两翼斜坡带是油气聚集的有利区带。     

阿尔泰南缘东段变形岩石磁组构分析

磁组构是指岩石磁化率的各向异性,磁组构方法已经被广泛应用于构造变形分析。阿尔泰南缘东段地区岩石磁组构特征是磁各向异性度P值大,反映本区总体韧性剪切变形强烈。萨尔布拉克—科克萨依脆性劈理化带和玛因鄂博韧性剪压构造带的E>0占优势,磁面理发育,部分样品磁线理发育,反映变形以压扁变形为主,主压应力方向为NE(NEE)向,伴随弱的左行剪切;达拉维孜—阿热勒托别韧性流变构造带和锡泊渡—富蕴深层次变晶糜棱岩带E>0和E<0均存在,磁面理和磁线理均发育,反映以剪切变形为主;其中达拉维孜—阿热勒托别构造带主压应力方向为NE(NEE)向,为左行剪切,而锡泊渡—富蕴构造带主压应力方向为SN向,为右行剪切。在达拉维孜—阿热勒托别构造带中的哈腊苏铜矿和卡拉先格尔—老山口一带一些叠加蚀变矿化的强变形岩石的P值明显减小,说明在韧性变形之后发生过矿化热液作用导致磁化率各向异性发生了均一化。结合区域构造分析,可以认为萨尔布拉克—科克萨依构造带、达拉维孜—阿热勒托别构造带和玛因鄂博构造带构成一个完整的板块碰撞聚合带,而锡泊渡—富蕴构造带可能为在古生代期间被强烈改造的具有前震旦纪结晶基底的微大陆的残留(或断片)。     

江西相山铀矿田地貌剥蚀特征及其控矿意义——磷灰石裂变径迹证据

本文以数字高程(DEM)地貌特征分析为基础,采用磷灰石裂变径迹测试和温度-时间反演模拟研究,分析江西相山铀矿田铀成矿后剥蚀程度的差异性,结合已知矿床的成矿特征,探讨地貌剥蚀程度与矿体保存之间的关系,为区域找矿提供指导.通过DEM合成图像和水系分布特征,表明相山铀矿田是一个遭受中等侵蚀的地貌区,相山主峰南北和东西侧地貌侵蚀差异特征明显.统计分析表明,已经发现的铀矿床、点的分布与次级火山机构关系密切,相山南部的次火山机构剥蚀较深,西部次火山机构剥蚀相对弱,而北部和西北部则处于中等剥蚀程度.磷灰石裂变径迹测试结果表明,相山铀矿田的南部和东部开始剥蚀的时间早于西部,但晚于相山主峰的剥蚀.利用磷灰石的裂变径迹长度和温度参数,进一步开展了温度-时间的反演模拟研究,结果显示相山西部快速隆升发生于40~60 Ma之间,相山南部和东部的快速隆升发生于60~75 Ma之间,相山主峰的快速隆升发生于75~100 Ma之间,表明相山主峰、相山东部及南部较西部经历了较长时间的剥蚀.结合现今区域地质体出露特征及铀矿化蚀变类型的空间展布规律、成矿深度的估算等,推测相山铀矿田东部和南部剥蚀程度较深,早期可能形成的中低温铀矿体被剥蚀殆尽;北部剥蚀程度中等,地表出露了形成深度稍深的碱交代蚀变矿床;而西部剥蚀程度较低,地表发育浅部低温成矿的酸交代蚀变铀矿床.据此推断,相山铀矿田的西部深部具有很好的找矿潜力.     

Benthic nutrient fluxes in the intertidal flat within the Changjiang （Yangtze River） Estuary

In an annual cycle from March 2005 to February 2006, benthic nutrient fluxes were measured monthly in the Dongtan intertidal flat within the Changjiang （Yangtze River） Estuary. Except for NH4^＋, there always showed high fluxes from overlying water into sediment for other four nutrients. Sediments in the high and middle marshes, covered with halophyte and consisting of macrofauna, demonstrated more capabilities of assimilating nutrients from overlying water than the low marsh. Sampling seasons and nutrient concentrations in the overlying water could both exert significant effects on these fluxes. Additionally, according to the model provided by previous study, denitrification rates, that utilizing NO3- transported from overlying water （Dw） in Dongtan sediments, were estimated to be from -16 to 193 μmol·h^-1·m^-2 with an average value of 63 μmol·h^-1·m^-2 （n=18）. These estimated values are still underestimates of the in-situ rates owing to the lack of consideration of DN, i.e., denitrification supported by the local NO3^- production via nitrification.     

Heavy metal contamination of cultivated wetland soils along a typical plateau lake from southwest China

Topsoil (0–20 cm) samples were collected from four cultivated wetlands including Northern and Western Fields (about 30-year tillage), and Southern and Western Fields (about 20-year tillage) along the Yilong Lake of China in October, 2005. Total concentrations of As, Cr, Cu, Pb, and Zn were determined using the inductively coupled plasma atomic absorption spectrometry in order to assess contamination status in four sampling plots. Results showed that the average concentrations of these heavy metals in Northern and Western Fields were significantly (P < 0.01) higher than those in reference soils, while they were generally lower in Southern and Eastern Fields. All the mean concentrations of heavy metals were lower, except that the average concentrations of As and Cr in Northern Field slightly exceeded the soil quality guidelines. The contamination indices showed low-contamination levels for As, Cu, Pb, and Zn in Northern and Western Fields, while no contamination levels could be observed in Southern and Eastern Fields. As for Cr, they showed moderate-contamination levels in Northern Field, but low or no contamination levels in other three Fields. The integrated contamination index values indicated Northern and Western Fields were moderately contaminated, while Southern and Eastern Fields were less contaminated. The same contamination sources of these heavy metals were identified in these fields using factor- and cluster analysis.     

Altitudinal trends in δ<Superscript>13</Superscript>C value,stomatal density and nitrogen content of <Emphasis Type="Italic">Pinus tabuliformis</Emphasis> needles on the southern slope of the middle Qinling Mountains,China

In this study, a coniferous tree species (Pinus tabuliformis Carr.) was investigated at a moderate-altitude mountainous terrain on the southern slope of the middle Qinling Mountains (QLM) to detect the trends in carbon isotope ratio (δ¹³C), leaf nitrogen content (LNC) and stomatal density (SD) with altitude variation in north-subtropical humid mountain climate zone of China. The results showed that LNC and SD both significantly increased linearly along the altitudinal gradient ranging from 1000 to 2200 m, whereas leaf δ¹³C exhibited a significantly negative correlation with the altitude. Such a correlation pattern differs obviously from that obtained in offshore low-altitude humid environment or inland high-altitude semi-arid environment, suggesting that the pattern of increasing δ¹³C with the altitude cannot be generalized. The negative correlation between δ¹³C and altitude might be attributed mainly to the strengthening of carbon isotope fractionation in plants caused by increasing precipitation with altitude. Furthermore, there was a remarkable negative correlation between leaf δ¹³C and LNC. One possible reason was that increasing precipitation that operates to increase isotopic discrimination with altitude overtook the LNC in determining the sign of leaf δ¹³C. The significant negative correlation between leaf δ¹³C and SD over altitudes was also found in the present study, indicating that increases in SD with altitude would reduce, rather than enhance plant δ¹³C values.     

Decrepitation Thermometry and Compositions of Fluid Inclusions of the Damoqujia Gold Deposit,Jiaodong Gold Province,China:Implications for Metallogeny and Exploration

The recently discovered Damoqujia (大磨曲家) gold deposit is a large shear zone-hosted gold deposit of disseminated sulphides located in the north of the Zhaoping (招平) fault zone, Jiaodong (胶东) gold province, China. In order to distinguish the temperature range of cluster inclusions from different mineralization stages and measure their compositions, 16 fluid inclusions and 5 isotopic geochemistry samples were collected for this study. Corresponding to different mineralization stages, the multirange peaks of quartz decrepitation temperature (250-270, 310-360 and 380-430℃(2) indicate that the activity of ore-forming fluids is characterized by multistage. The ore-forming fluids were predominantly of high-temperature fluid system (HTFS) by CO2-rich, and SO2-4-K type magmatic fluid during the early stage of mineralization and were subsequently affected by low-temperature fluid system (LTFS) of CH4-rich, and Cl--Na /Ca2 type meteoric fluid during the late stage of mineralization. Gold is transferred by Au-HS- complex in the HTFS, and Au-Cl- complex can be more important in the LTFS. The transition of fluids from deeper to shallow environments results in mixing between the HTFS and LTFS, which might be one of the most key reasons for gold precipitation and large-scale mineralization. The ore-forming fluids are characterized by high-temperature, strong-activity, and superimposed mineralization, so that there is a great probability of forming large and rich ore deposit in the Damoqujia gold deposit. The main bodies are preserved and extend toward deeper parts, thereby suggesting a great potential in future.     

新地震区划图地震构造区划分的原则和方法——以中国东部中强地震活动区为例

在新编全国地震区划图潜在震源区划分工作中,采用了三级划分的技术思路,即在地震统计区内先划分出地震构造区,以控制地震统计区内地震构造和地震活动的差异性,然后,在地震构造区内再进行潜在震源区划分。地震构造区划分是新编全国地震区划图潜在震源区划分工作中的一个关键环节。本文论述了地震构造区的定义、作用、划分原则和依据等。介绍了中国东部地区地震构造区划分方案,并以东北地震区和华南沿海地震带为例,对地震构造区划分方案进行了详细论述。     

The down-faulted basin zone and high disaster risk zone in Shanxi Province, China

Introduction Since 1920s, with increasing awareness of disaster prevention and reduction in various as-pects and gradually deepened development of International Decade for Natural Disaster Reduction (IDNDR) activities in China, the role and position of disaster risk analysis and risk management work are increasingly prominent. In the final report of the IDNDR activities in 1999, the Science and Technology Committee of IDNDR had presented 5 challenge fields, which the international dis…     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.