蚌埠隆起老山片麻状二长花岗岩的成岩与变质特征及其地质意义

蚌埠隆起是华北地块上中生代地质研究的一个十分难得的地质单元,它映现了中国东部最重要的两次构造运动(印支运动和燕山运动)所引起的岩浆作用和变质作用,其中,老山片麻状二长花岗岩就是一个代表性产物。该花岗岩的岩相学、地球化学以及锆石学的系统研究显示,它具有两个不同的岩石学特征:①它的斜长石斑晶的牌号(An_(14～18))、相对低硅高钙的主量元素、富Ba、Sr和贫Rb及无Eu负异常的微量元素等特征,以及它所含的岩浆锆石({211}锥面发育、较强的CL亮度及韵律环带构造、包含残核、正常的Hf含量)的U-Pb年龄(223.1 Ma)和ε_(Hf)(t)值(平均值为-14.2),表明它应该是印支运动的同造山花岗岩;②它的片麻状构造(斑晶矿物压扁拉长、各种交代—熔蚀结构)和变质结晶锆石({101}锥面发育、较弱的CL亮度、较高的Hf含量)的U-Pb年龄(156.2 Ma)和ε_(Hf)(t)值(平均值为-17.8),说明它在燕山运动中遭受了同造山变质作用。因此,老山片麻状二长花岗岩的存在,一方面使得大别造山带作为印支期陆陆碰撞造山带的岩石学记录完整,同时也制约了华南与华北两大板块之间碰撞挤压作用峰期的准确时间;另一方面,对燕山运动的起始时间(约170 Ma左右)和挤压高峰出现的时间(155±3 Ma)作出了合理的推断。这两方面的研究结果对于深入了解中国东部中生代的构造演化、岩石类型、成矿作用的现象及其机制具有深远的意义。     

天山-兴蒙造山带主要钼矿床Re-Os定年和地质意义

天山-兴蒙钼矿带是中亚成矿域的重要组成部分,该成矿带主要呈近东西向分布;本文通过对天山-兴蒙钼矿带4个典型矿床Re-Os同位素精确定年,结合前人区域动力学背景的研究,揭示天山-兴蒙造山带钼矿床的成矿作用主要与岩浆侵入形成的花岗岩热液作用有关,并识别出兴蒙造山带3期岩浆活动、钼成矿作用和构造热事件;Re-Os定年结果揭示出晚古生代铜-钼矿床与俯冲-增生作用有关,三叠纪钼的成矿形成于西伯利亚板块与塔里木-华北克拉通碰撞背景下,而侏罗纪-早白垩世的钼成矿作用与古太平洋板块西向俯冲作用有关。     

新疆兴地Ⅱ号铜镍矿床辉长岩SIMS锆石U-Pb年龄和地质意义

兴地Ⅱ号铜镍矿床为岩浆多期次分异、侵位形成的基性-超基性杂岩体;本文通过对兴地辉长岩进行SIMS锆石U-Pb精确测年,获得²⁰⁶Pb/²³⁸U加权平均年龄为737±2 Ma,该数据表明兴地Ⅱ号杂岩体形成于南华纪,并非形成于中元古代。塔里木北缘广泛发育的碳酸岩、基性-超基性岩和双峰式火山岩,为Rodinia 超大陆长期的和持续的裂解产物。     

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。     

1965-2015年新疆夏季不同等级降水的空间分布特征

根据新疆51个台站1965-2015年夏季逐日降水资料,将降水划分为小雨、中雨及大雨3个等级,分析了新疆近51 a夏季不同等级降水量、降水日数及降水强度的空间分布特征,并讨论了各等级降水日、降水量及降水强度与总降水量的空间相似程度以及各等级降水对夏季总降水的贡献。结果表明：新疆降水主要集中在夏季,并以小雨为主。以天山山脉为界,南北两疆降水空间分布存在明显差异,北疆夏季降水量（日）占年降水量（日）的36%~45%（36%~39%）,南疆夏季降水量（日）占年降水量（日）的51%~63%（48%~60%）;新疆夏季不同等级降水量、降水日及降水强度的空间分布不均匀。新疆夏季总降水量与各等级降水量的空间相似系数最为密切,与各等级降水强度的空间相似系数相对较小;新疆夏季小雨贡献率最大,中雨其次,大雨最小,夏季降水量和降水日的变化主要受小雨的影响。     

小极距高密度电法在城镇供水管漏水检测中的应用

城市地下供水管破损漏水检测,一般使用听音工具和设备来进行,这种需要漏水点传出声音的检测方式,效果往往并不理想。使用一种新的检测方法,无需利用声音,而是通过供水管漏水造成地层电性变化这一特征,使用小极距高密度电法查明管道漏水点位置。该检测方式不仅解决了常规方法难以查明的供水管漏水问题,而且拓展了小极距高密度电法的应用空间。     

Research Progress and Prospect on Reactivation of Ancient Landslides

The ancient landslide has endured long-term slope evolution which results in its complicated material and special rock-soil properties. The risk of ancient landslide reactivation is substantially increasing due to the increase of intensified human engineering activities and the frequency of extreme weather events. Many ancient landslides have been reactivated all over the world and led to serious fatalities and severe damage to many important engineering facilities such as transportation and hydropower engineering projects. On the basis of the analysis of the research situation about the ancient landslides at home and abroad, the main research advances were summarized including the regional developing laws and recognizing of the ancient landslides, the mechanics properties of ancient landslide body and related sliding zone, reactivation mechanism of ancient landslides, reactivating process and modeling analysis of ancient landslides, early recognization of ancient landslide reactivation, etc. To meet the demands of disaster prevention and reduction, three key scientific issues were put forward to be solved: ①automaticaly establishing the methodology and identification criterions for recognition of ancient landslide; ②revealing the reactivation mechanism of ancient landslide based on a new strength theory; ③establishing the early rapid recognition method and predictive model for ancient landslide reactivation. Solving the above mentioned scientific theory and methodology will facilitate the planning and site selection of major projects as well as the disaster prevention and reduction in ancient landslide developing areas.     

A modified three-dimensional ionospheric tomography algorithm with side rays

The three-dimensional ionospheric tomography (3DCIT) algorithm based on Global Navigation Satellite System (GNSS) observations have been developed into an effective tool for ionospheric monitoring in recent years. However, because the rays that come into or come out from the side of the inversion region cannot be used, the distribution of the rays in the edge and bottom part of the inversion region is scarce and the electron density cannot be effectively improved in the inversion process. We present a three-dimensional tomography algorithm with side rays (3DCIT-SR) applying the side rays to the inversion. The partial slant total electron content (STEC) of side rays in the inversion region is obtained based on the NeQuick2 model and GNSS-STEC. The simulation experiment results show that the algorithm can effectively improve the distribution of GNSS rays in the inversion region. Meanwhile, the iteration accuracy has also been significantly improved. After the same number of iterations, the iterative results of 3DCIT-SR are closer to the truth than 3DCIT, in particular, the inversion of the edge regions is improved noticeably. The GNSS data of the International GNSS Service (IGS) stations in Europe are used to perform real data experiments, and the inversion results show that the electron density profiles of 3DCIT-SR are closer to the ionosonde measurements. The accuracy improvement of 3DCIT-SR is up to 56.3% while the improvement is more obvious during the magnetic storm compared to the case of a calm ionospheric state .     

A comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr^–1.     

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.