高压超高压变泥质岩适用的地质温压计评述

高压超高压变泥质岩形成温压条件及p-T演化轨迹是高压超高压变质作用研究的重要内容。本文介绍了高压超高压变泥质岩适用的几种地质温压计,包括石榴子石-多硅白云母Fe~(2+)-Mg交换温度计、石榴子石-绿泥石Fe~(2+)-Mg交换温度计、硬绿泥石-绿泥石Fe~(2+)-Mg交换温度计、多硅白云母Si含量压力计、金红石Zr含量温度计、榍石Zr含量温压计、锆石Ti含量温度计、石英Ti含量温度计以及氧同位素温度计,并对上述温压计的适用条件及使用时的注意事项做了简要评述。     

Climatic Changes in the Dynamic Topography and Geostrophic Circulation of the Arctic Ocean

Doklady Earth Sciences - According to historical oceanographic data, anomalies in the dynamic topography during the winter period were calculated and two climatic stages of dynamic condition of the...     

Comparison of single- and dual-permeability models in simulating the unsaturated hydro-mechanical behavior in a rainfall-triggered landslide

Landslide-prone slopes in earthquake-affected areas commonly feature heterogeneity and high permeability due to the presence of cracks and fissures that were caused by ground shaking. Landslide reactivation in heterogeneous slope may be affected by preferential flow that was commonly occurred under heavy rainfall. Current hydro-mechanical models that are based on a single-permeability model consider soil as a homogeneous continuum, which, however, cannot explicitly represent the hydraulic properties of heterogeneous soil. The present study adopted a dual-permeability model, using two Darcy-Richards equations to simulate the infiltration processes in both matrix and preferential flow domains. The hydrological results were integrated with an infinite slope stability approach, attempting to investigate the hydro-mechanical behavior. A coarse-textured unstable slope in an earthquake-affected area was chosen for conducting artificial rainfall experiment, and in the experiment slope, failure was triggered several times under heavy rainfall. The simulated hydro-mechanical results of both single- and dual-permeability model were compared with the measurements, including soil moisture content, pore water pressure, and slope stability conditions. Under high-intensity rainfall, the measured soil moisture and pore water pressure at 1-m depth showed faster hydrological response than its simulations, which can be regarded as a typical evidence of preferential flow. We found the dual-permeability model substantially improved the quantification of hydro-mechanical processes. Such improvement could assist in obtaining more reliable landslide-triggering predication. In the light of the implementation of a dual-permeability model for slope stability analysis, a more flexible and robust early warning system for shallow landslides hazard in coarse-textured slopes could be provided.     

Use of photo-based 3D photogrammetry in analysing the results of laboratory pressure grouting tests

This paper presents a non-destructive, low-cost, photo-based, 3D reconstruction technique for characterizing geo-materials with irregular shapes of a relatively large size. After being validated against two traditional volume measurement methods, namely the vernier caliper method and the fluid displacement method for regular and irregular shapes, respectively, 3D photogrammetry was used to analyse the grout bulbs formed in laboratory pressure grouting tests. The reconstructed 3D mesh model of the sample provides accurate and detailed 3D vertex data, which allowed the volume, densification efficiency and bleeding behaviour of the grout bulbs to be analysed. Comparing the bulb section views at different grouting pressures also offers an intuitive observation of the grout development and propagation process. Moreover, the 3D vertex data and surface area included in the model are of great importance in validating numerical predictions of the pressure grouting process and analysing the interface shear resistance of grouted soil nails or anchors. Compared to existing approaches, the new 3D photogrammetry method possesses several key advantages: (a) it does not require expensive, specialized equipment; (b) samples are not destroyed or modified during testing; (c) it allows to reconstruct objects of various scales and (d) the software is public domain. Therefore, the adoption of this 3D photogrammetry method will facilitate research in the pressure grouting process and can be extended to other problems in geotechnical engineering.     

准噶尔盆地东北部侏罗系砂岩型铀矿成矿环境与找矿方向探讨

文章从蚀源区铀源条件、中新生代构造演化条件、侏罗系沉积建造特征及后生蚀变特征等角度分析了准噶尔盆地东北部砂岩型铀矿的区域成矿条件,认为卡拉麦里山的富铀花岗岩体为该区砂岩型铀成矿提供了丰富的铀源,中侏罗统含煤碎屑岩建造为砂岩型铀成矿提供了良好的容矿空间,侏罗纪以来多期次不整合面的发育为含铀含氧水的渗入改造提供了通道及时间,为大规模层间氧化带的发育提供了物质基础。研究结果显示,卡拉麦里山北坡为最有利的砂岩型铀矿找矿部位,其主要找矿目标层位为中侏罗统头屯河组,找矿类型为层间氧化带型。     

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。     

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.     

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.