干旱地区水循环过程中的地下水化学特征研究——以蒙古国前巴音钼矿水源地为例

地下水的水化学类型在径流过程中会不断变化,并在径流与赋存的不同状态下体现出不同特征,体现出不同的地下水运移规律。本文以蒙古国前巴音钼矿水源地为例,选取水源地范围内的19个钻孔水样进行分析,取得了主要阴阳离子、TDS和水化学类型等数据。通过对地下水化学的空间分布特征、TDS值的变化和主要阴阳离子的演化规律进行研究发现:研究区水源地地下水补给来源为大气降水,沟谷东部地区的水循环强度高于西南部地区,外围地区最弱,地下水的水平径流较弱小于垂向;受水循环强度影响沟谷东部形成地矿化度的HCO_3型水,西南部形成HCO_3·Cl·SO_4、HCO_3·SO_4·Cl、HCO_3·SO_4型水,沟谷外围地区形成SO_4·HCO_3·Cl、Cl·SO_4·HCO_3、SO_4·HCO_3、Cl·SO_4型水,干旱地区水循环强度和补径排条件的差异会形成不同的水化学类。     

Performance of GPS and GPS/SINS navigation in the CE-5T1 skip re-entry mission

A CE-5T1 spacecraft completed a high-speed skip re-entry to the earth after a circumlunar flight on October 31, 2014. In addition to the strapdown inertial navigation system (SINS), a lightweight GPS receiver with rapid acquisition was developed as a navigation sensor in the re-entry capsule. The GPS receiver effectively solved the poor accuracy problem of long-term navigation using only the SINS. In contrast to ground users and low-earth-orbit spacecraft, numerous factors, including high altitude and kinetic characteristics in high-speed skip re-entry, are important for GPS positioning feasibility and were presented in accordance with the flight data. GPS solutions started at nearly 4900 km orbital altitude during the phases of re-entry process. These solutions were combined by an inertial measurement unit in a loosely coupled integrated navigation method and SINS navigation initialization. A simplified GPS/SINS navigation filter for limited resources was effectively developed and implemented on board for spacecraft application. Flight data estimation analyses, including trajectory, attitude, position distribution of GPS satellite, and navigation accuracy, were presented. The estimated accuracy of position was better than 42 m, and the accuracy of velocity was better than 0.1 m/s.     

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.     

Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin,China

Natural Hazards - Droughts have caused many damages in many countries and might be aggravated around the world. Therefore, it is urgent to predict and monitor drought accurately. Soil moisture and...     

Destressing Mechanics Effect of Surrounding Rock Induced by Blasting Precondition at Deep Drift Development

Geotechnical and Geological Engineering - Using a detailed modeling simulation technology, a mechanics simulation model was constructed, including multiple development rounds, stress relief holes...     

Heat Islands of Small Towns and Villages in Moscow Region

Doklady Earth Sciences - Surface urban heat island, which are thermal anomalies in the field of surface temperatures, have been studied for the first time for different towns and villages of Moscow...     

New Data on the Chronostratigraphy of the Upper Pleistocene Loess–Soil Series in Southwestern Siberia

Doklady Earth Sciences - We have analyzed the modern status of the chronostratigraphic differentiation of the loess–paleosoil series in southwestern Siberia and the results of luminescence...     

方解石与含硅流体的水-岩反应实验及其对“硅化碳酸盐岩”储层成因的启示

近年来的油气勘探表明, 含硅热液是碳酸盐岩层系中一种重要的溶蚀性流体, 查明其与碳酸盐岩的水-岩反应机理是揭示“硅化碳酸盐岩”储层发育机制并实现储层分布预测的基础和关键问题之一。文章采用熔融毛细硅管和水热反应釜为反应腔,开展了200~375℃范围内方解石和含硅流体的水岩反应实验。利用原位拉曼光谱技术在线描述反应过程中体系组成的变化,对于淬火后的固相样品,则采用扫描电镜—能谱分析进行形貌观测和成分鉴定。首先,查明了含硅流体与方解石脱碳反应发生的温度条件。方解石和含硅流体在275℃以上反应形成CO₂,固相为非硅灰石的钙硅酸盐,其结构有待进一步揭示。该结果表明单纯的硅质组分难以在储层温度条件下与灰岩发生反应;其次,提出高盐度、富CO2流体作用是造成灰岩溶蚀的重要因素;最后, CO₂的存在能够促进硅质（含石英）沉淀。在上述实验认识的基础上,结合前人研究结果探讨了塔里木盆地顺托果勒地区“硅化碳酸盐岩”储层的发育机制。含硅热液沿深大断裂上移,途径震旦系—下奥陶统白云岩层系,其中的硅质组分将与白云石反应形成富镁硅酸盐和CO₂。CO₂是重要的酸性组分,有利于鹰山组碳酸盐的溶蚀和孔隙的保存。流体温度和压力的降低以及CO₂的存在促进了石英沉淀,并形成了大量的石英晶间孔隙。