重庆秀山南沱冰期后的海陆环境变化

扬子地区发育地层所记录的南沱冰期在时间上与Marinoan冰期相当,被认为是"雪球事件"的产物,受到广泛关注。借助于重庆秀山长河桥剖面的南沱组冰碛岩上覆陡山沱组盖帽白云岩和页岩样品的精细采集,选取冰碛岩之上2.5 m内的地层进行同位素比值和元素含量测试,并尝试性的使用酸不溶物的元素地球化学数据,对冰期后可能出现的环境变化进行了综合分析,结果表明:盖帽白云岩C同位素数据基本符合海水分层混合模式特征;U/Th值也反映出该地区在雪球后经历了由缺氧-贫氧环境向贫氧-氧化环境的迅速转变,可能反映了雪球后缺氧富有机质的深部大洋水随上升洋流上涌并被迅速氧化的过程;强烈的Eu正异常的出现,可能与埃迪卡拉纪海底火山、热液活动频繁出现或近源热液活动相关;盖帽碳酸盐岩样品中出现Ce轻微负异常,表明其形成于弱氧化环境;87Sr/86Sr值和Y/Ho值均呈现伴有大量陆源碎屑物输入的特点;盖帽碳酸盐岩的酸不溶物可以反映当时的大陆风化背景,其化学蚀变指数（CIA）稳定在72左右,说明当时的大陆环境具有温暖湿润的特征,化学风化作用强度中等。综上所述,在南陀冰期结束后,秀山长河桥剖面的沉积环境经历了缺氧-贫氧环境向贫氧-氧化环境的迅速转变,同时,随着上升洋流的出现原有的冰期海水分层被迅速破坏,并可能伴随着地表径流的不断增强,同时热液活动在这一时期也频繁发生。在这一时期,剖面附近的大陆环境也迅速由冰期过渡为温暖湿润的环境。     

基于岩石图像深度学习的岩性自动识别与分类方法

岩石岩性的识别与分类对于地质分析极为重要,采用机器学习的方法建立识别模型进行自动分类是一条新的途径。基于Inception-v3深度卷积神经网络模型,建立了岩石图像集分析的深度学习迁移模型,运用迁移学习方法实现了岩石岩性的自动识别与分类。采用此方法对所采集的173张花岗岩图像、152张千枚岩图像和246张角砾岩图像进行了学习和识别分类研究,通过训练学习建立岩石图像深度学习迁移模型,并分别采用训练集和测试集中的岩石图像对模型进行了检验分析。对于训练集中的岩石图像,每组岩石分别用3张图像测试,三种岩石的岩性分类均正确,且分类概率值均达到90%以上,显示了模型良好的鲁棒性;对于测试集中的岩石图像,每组岩石分别采用9张图像进行识别分析,三种岩石的岩性分类均正确,并且千枚岩组图像分类概率均高于90%,但是花岗岩组2张图像和角砾岩组的1张图像分类概率值不足70%,概率值较其他岩石图像低,推测其原因是训练集中相同模式的岩石图像较少,导致模型的泛化能力减小。为了提高识别精确度,对准确率较低的岩石图像进行截取,分别取其中的3张图像加入训练集进行再训练,增加与测试图像具有相同模式的训练样本;在新的模型中,对3张图像进行二次检验,测试概率值均达到85%以上,说明在数据足够的状况下模型具有良好的学习能力。与传统的机器学习方法相比,所提出的岩石图像深度学习方法具有以下优点:第一,模型通过搜索图像像素点提取物体特征,不需要手动提取待分类物体特征;第二,对于图像像素大小,成像距离及光照要求低;第三,采用适当的训练集可获得较好的识别分类效果,并具有良好鲁棒性和泛化能力。     

滇西芦子园远程矽卡岩Pb-Zn-Fe(Cu)多金属矿床流体包裹体初探及矿床成因探讨

滇西镇康芦子园是"三江"成矿带保山地块内迄今发现的唯一超大型Pb-Zn-Fe(Cu)多金属矿床,是区内系列同类层控热液铅锌矿床的典型代表。矿体呈似层状、脉状及透镜状产于寒武系碳酸盐岩建造的矽卡岩和大理岩层间破碎带,矿石构造以条带状、浸染状和脉状-网脉状为主要特征;围岩蚀变复杂、分带明显,由下至上依次为石榴子石-透辉石-透闪石-阳起石化带→绿泥石-绿帘石-阳起石-蔷薇辉石化带→碳酸盐-大理岩化带。矿床成矿流体从早期到晚期经历了多个矿化阶段。本文选取了该矿床早矽卡岩阶段、晚矽卡岩阶段、石英硫化物阶段和石英碳酸盐阶段的多种脉石矿物及闪锌矿进行了系统的流体包裹体研究。结果显示,早矽卡岩阶段发育微溶CO_2富液相和纯液相水溶液包裹体,并含大量K~+、Na~+、Ca~(2+)、F~-和Cl~-和少量SO_4~(2-),气相成分主要为H_2O、CO_2及少量CH_4和N_2,包裹体均一温度为233.6~315.6℃,盐度为10.6%~17.6%NaCleqv;晚矽卡岩阶段发育含CO_2和子矿物三相包裹体,均一温度214.9~388.0℃,盐度5.9%~16.4%NaCleqv;石英硫化物阶段发育含CO_2的水溶液包裹体,气相成分为CH_4、H_2O和少量CO_2,均一温度150.0~285.0℃,盐度为2.5%~13.8%NaCleqv;石英碳酸盐阶段为单一成分的水溶液包裹体,均一温度为105.0~187.5℃,盐度为0.5%~12.3%NaCleqv。结合H-O同位素研究表明,成矿流体最初来源于具中高温、中高盐度、高K、Na,富CO_2、Cl、F等特征的深部岩浆热液,在石英硫化物阶段开始有大气降水混入,演化为中阶段中低温、低盐度、贫CO_2的热液流体,至成矿晚阶段转化为以大气降水占主导。该矿床成矿环境的改变、流体混合以及流体的沸腾作用可能是导致成矿物质富集沉淀的重要机制。综合矿床地质特征、成矿流体包裹体和HO同位素研究认为,该矿床为与陆陆碰撞造山和深部隐伏岩体有关的远程矽卡岩成矿系统。     

华北地台北缘集宁地区古元古代片麻状石榴花岗岩的深熔成因及地质意义

本文对华北克拉通北缘集宁地区空间上密切共生的片麻状石榴花岗岩和孔兹岩系富铝片麻岩的岩相学、地球化学及年代学特征进行了对比研究。SHRIMP锆石U-Pb定年方面,在富铝片麻岩中获得了1910±10Ma和1839±13Ma变质锆石年龄,在片麻状石榴花岗岩中获得了1919±17Ma的变质重结晶锆石年龄。在石榴花岗岩的石榴石包裹体中识别出与富铝片麻岩相对应的进变质阶段(M1)和峰期阶段(M2)的矿物组合,由此确认富铝片麻岩的变质作用和导致石榴花岗岩形成的深熔作用是同一构造热事件的产物。通过对二者变质作用演化及特征变质矿物的对比,认为深熔作用主要发生在峰期后等温降压阶段(M3),石榴花岗岩中的石榴石为深熔作用过程中的残留矿物相或转熔矿物相,而石榴花岗岩则是混合有大量残留矿物相的熔体结晶的产物。对片麻状石榴花岗岩和富铝片麻岩的地球化学组成特征进行了对比分析,片麻状石榴花岗岩既有一定的继承性,又有十分明显的变异性。变异性表现为:1)石榴花岗岩主量和微量元素含量分布极不均匀,微量元素含量普遍低于源岩(Cs、Rb、Th、U、Nb、Ta、Zr、Hf等);2)大离子亲石元素Cs和生热元素U、Th亏损明显,Sr相对富集;3)高场强元素Nb、Ta、P、Ti的明显亏损;4)铕异常变化大,存在铕富集型、铕平坦型和铕亏损型共存的稀土配分曲线的岩石,这是深熔成因石榴花岗岩最突出的表现,也可能是原地-半原地深熔花岗岩的主要地球化学标志。综合区域上的地质资料,认为深熔作用与碰撞后伸展构造背景下基性岩浆底侵事件有关。     

A review of advances in China’s flash flood early-warning system

This paper summarizes the main flash flood early-warning systems of America, Europe, Japan, and Taiwan China and discusses their advantages and disadvantages. The latest development in flash flood prevention is also presented. China’s flash flood prevention system involves three stages. Herein, the warning methods and achievements in the first two stages are introduced in detail. Based on the worldwide experience of flash flood early-warning systems, the general research idea of the third stage is proposed from the viewpoint of requirements for flash flood prevention and construction progress of the next stage in China. Real-time dynamic warning systems can be applied to the early-warning platform at four levels (central level, provincial level, municipal level, and county level) . Through this, soil moisture, peak flow, and water level can be calculated in real-time using distributed hydrological models, and then flash flood warning indexes can be computed based on defined thresholds of runoff and water level. A compound warning index (CWI) can be applied to regions where rainfall and water level are measured by simple equipment. In this manner, flash-flood-related factors such as rainfall intensity and antecedent and cumulative rainfall depths can be determined using the CWI method. The proposed methodology for the third stage could support flash flood prevention measures in the 13th 5-Year Plan for Economic and Social Development of the People’s Republic of China (2016–2020). The research achievements will serve as a guidance for flash flood monitoring and warning as well as flood warning in medium and small rivers.     

南极南设得兰群岛海域地质新进展:“海洋六号”南极科考初步成果

2016—2017年跨年度执行的中国第33次南极科考“海洋六号”航次, 首次获取了南极半岛南设得兰群岛北部海域、布兰斯菲尔德海峡以及欺骗岛三个区域的17 850 km2范围内多波束全覆盖高精度水深数据, 地形分辨率高于目前公开的国际数据。多道地震结果显示调查区多处存在天然气水合物地球物理标志BSR。地热测量热流值表明南设得兰群岛以北热流值与全球海洋热流值数据吻合, 不存在明显异常, 群岛以南热流值数据偏高。沉积物样品为典型的冰海沉积物, 在南设得兰群岛与南设得兰海沟之间, 沉积物以粉砂-砂为主, 夹杂砾石, 随水深增加, 沉积物粒径变细; 而在布兰斯菲尔德海区表层沉积物分布具有不对称性, 海槽南部陆架-上陆坡以及海槽东北和相邻陆架, 沉积物以砾石、砂质为主; 海槽和下陆坡为粉砂质硅质软泥。沉积物顶空气样品现场测试结果表明, 表层沉积物50 cm左右深度内的甲烷含量没有明显的差异性, 但在岛坡深水区两个测站甲烷含量在270 cm 左右深度时都存在局部高异常, 且两者总甲烷含量的变化趋势有一定的相似性, 可能与水合物分解迁移有关。     

基于BOTDR技术的深埋岩溶土洞监测分析

岩溶洞穴发育成地表塌陷影响因素复杂。为了获得岩溶土洞监测标识区域的应力特征,在太沙基松动土压力理论基础上,对岩溶土洞发育历程中松动区竖向土压力进行了分析。松动区的空间位置由洞穴特征及其上方岩土介质的物理、力学性质决定;平面应变和非平面应变条件下,松动区竖向土压力的分布特征差异明显。根据岩溶土洞上方松动区竖向土压力的分布特征,结合BOTDR技术优点,提出应用BOTDR技术监测岩溶土洞的发育,针对光纤监测系统设计中光纤布线型式进行了分析。岩溶土洞应力场变化特征的定量研究对光纤传感技术应用于岩溶土洞监测设计提供理论依据和指导。      

Analysis of estimated satellite clock biases and their effects on precise point positioning

This study provides a systemic analysis to identify the biases in estimated satellite clocks and illustrates their effects in precise point positioning (PPP). First, the precise satellite clock estimation method considering pseudorange and carrier phase hardware delays is derived. Two methods for satellite clock estimation are compared, and their equivalency is discussed. The results show that apart from the well-known constant code hardware biases, the time-variant phase hardware biases are also absorbed by the estimated clocks. Also, the satellite clocks contain biases caused by modeling errors. To analyze the effects of these biases, they are grouped into initial clock biases (ICBs) and time-dependent biases (TDBs). Then, a detailed analysis of the impact of the biases on PPP-based troposphere and coordinate estimates is conducted. The experimental analysis demonstrates that TDBs affect positioning and tropospheric estimates, and their impacts are more significant in the static mode. The ICBs affect coordinate accuracy, zenith total delay mean bias, and its standard deviations only at the millimeter level for kinematic and static PPP, which is negligible. However, the ICBs affect the convergence period for both static and real kinematic PPP, and the magnitude of their impact largely depends on data quality. Note that satellites clocks are generally estimated with the P1/P2 and L1/L2 ionospheric-free combinations, and that hardware-specific parts of ICBs and TDBs cancel if users employ the same type of observables as the clock providers. Otherwise, the effects of biases cannot be ignored, especially for triple-frequency applications. Also, modeling-specific parts of ICBs and TDBs are significant in real-time clocks, which also affect user applications. Our conclusion is applicable for understanding the effects of these biases.     

Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data

The accurate mapping of urban housing prices at a fine scale is essential to policymaking and urban studies, such as adjusting economic factors and determining reasonable levels of residential subsidies. Previous studies focus mainly on housing price analysis at a macro scale, without fine‐scale study due to a lack of available data and effective models. By integrating a convolutional neural network for united mining (UMCNN) and random forest (RF), this study proposes an effective deep‐learning‐based framework for fusing multi‐source geospatial data, including high spatial resolution (HSR) remotely sensed imagery and several types of social media data, and maps urban housing prices at a very fine scale. With the collected housing price data from China's biggest online real estate market, we produced the spatial distribution of housing prices at a spatial resolution of 5 m in Shenzhen, China. By comparing with eight other multi‐source data mining techniques, the UMCNN obtained the highest housing price simulation accuracy (Pearson R = 0.922, OA = 85.82%). The results also demonstrated a complex spatial heterogeneity inside Shenzhen's housing price distribution. In future studies, we will work continuously on housing price policymaking and residential issues by including additional sources of spatial data.