深基坑水平位移监测方法与精度浅析

在深基坑变形监测中,水平位移是反映基坑变形最直接的物理量,如何及时有效地获得准确反映基坑变形的位移数据尤为重要.监测研究以安徽省节水技术推广研究中心大楼基坑变形监测为例,使用高精度徕卡0.5″全站仪,采用棱镜强制对中的方法,对该深基坑的支护顶或坡体的特定方向进行水平位移监测.同时,对监测中工作基点和监测点布置方法、监测流程以及精度优化等进行了探讨,为后期相关工程的研究和实施提供参考.     

视准线小角法在基坑水平位移监测中的优化应用

根据实际工程需要提出了基坑水平位移监测的新方法,该方法综合运用了视准线小角法和观测点设站法的优点,讲述了监测步骤及数据处理方法,并将该方法应用于工程实例验证其有效性。结论表明,本文提出的方法具有一定的实用性。     

G PS 在石门子水库副坝变形监测中的应用

以石门子水库副坝（土石坝）变形监测为例,介绍了其监测方法及对观测数据的处理和分析,采用G PS平面拟合高程进行水库竖向位移监测,其精度与三等水准精度相当,而且可以将平面与竖向位移同时观测,大大提高了测量效率,节省了人力和物力。     

基坑水平位移监测新方法及效用研究

基于水平角度、水平距离观测值与基坑水平位移之间的内在相关关系,提出了一种"自由设站余弦定理法",该方法具有可以根据工程现场实际情况自由设站、无需加密平面控制点、无需测试人员后视定向、作业过程更加简单快捷、通用性更强等优点;模拟试验数据计算结果表明,该方法可以取得较高的监测精度,完全可以满足基坑水平位移监测工作的需要。     

基坑深层水平位移曲线分析研究

深层水平位移监测是基坑监测的重要组成部分。本文介绍了基坑深层水平位移监测的工作原理,通过基坑监测工程实例,分析研究了围护结构和土质边坡条件下实测的深层水平位移曲线。研究结果表明,曲线的变形机制与围护结构、工程地质条件有密切的关系,对今后类似的监测和资料分析具有参考意义。     

三维激光扫描在土石坝表面及扁钢的高精度变形监测中的应用研究

当前土石坝的表面变形采用测量机器人监测系统为主、大坝GNSS监测系统为辅的监测方法.对于坝面的扁钢隆起量的监测,仅依靠传统的测量手段,如皮尺或测距轮,测量精度较低,测量范围较小,无法直观地反映出扁钢的隆起状态.对于坝体水下部分的监测,仍无有效的监测手段,不利于大坝整体的变形监测分析.对于坝体水上部分坝面的变形监测,仅依靠布设在大坝表面的少量监测点显然是不够的,容易导致以点带面的窘境,因此,本文研究了一种独特的数据圆形裁剪拼接方式,同时探究出一种针对土石坝坝面扁钢快速提取的方法,成功地将三维激光扫描新技术推广到土石坝大坝坝面变形监测.最终结合自主研发的三维可视化分析平台,将土石坝坝面变形监测的丰富信息呈现出来,用于决策.     

暗挖法施工深层水平位移变化规律的探究与分析

介绍了暗挖法施工深层水平位移的计算方法,结合工程实测数据深入探究分析了暗挖法施工深层水平位移的变化规律,给出了一些有益的结论和建议。     

基于空间基准线的水平位移监测方法探讨

常规水平位移监测方法,如视准线法、小角度法、极坐标法等受场地条件限制较大,应用中受到一些限制。文中提出一种新的水平位移监测方法——空间基准线法,此方法受场地限制小,设站灵活,并能保证监测的精度和可靠性,已在工程实践中取得良好的效果。     

灰色-马尔科夫模型在大坝内部变形预测中的应用

传统的灰色-马尔科夫模型一般都是等时距的。针对样本不能满足等时距的需要,通过一定方法将样本等时距化,用多变量灰色模型MGM(1,n)与马尔科夫转移矩阵相结合对等时距样本进行建模,建立非等时距的灰色-马尔科夫模型。文中结合某大坝内部水平位移实测数据,用此模型进行建模。结果表明,灰色-马尔科夫模型不仅比灰色模型的拟合精度高,而且提高了预测精度。     

不同饱和水汽压模型对GNSS反演可降水量的影响分析

地基全球卫星导航系统（GNSS）水汽反演过程中需要大气加权平均温度T_m的参与,而饱和水汽压E_s作为T_m计算过程中的一个重要变量影响着T_m,因此E_s将会间接地影响大气可降水量（PWV）的反演精度.针对目前地基GNSS水汽反演研究中普遍采用的三种不同的饱和水汽压模型（Magnus-Tetens模型、BUCK模型、Goff-Gratch模型）,本文就不同的饱和水汽压模型参与反演是否会引起水汽反演结果的差异进行了研究.以香港为研究区域,利用GAMIT解算了2016年旱雨两季（2、7月）的天顶湿延迟（ZWD）,同时利用king's park探空站的探空数据通过数值积分计算得到旱雨两季（2、7月）的T_m,然后严格参照反演步骤编程模拟计算旱雨两季（2、7月）每天的PWV.最后对比并分析了不同饱和水汽压模型参与计算对T_m和PWV的影响及原因,结果表明:三种饱和水汽压模型参与计算得到的PWV与真值（探空站计算得到的PWV）之间不存在具有统计意义的显著性差异,因此均可被用来提供计算T_m时所用到的饱和水汽压E_s,但是通过对比分析发现部分研究人员将BUCK模型中的变量T当作露点温度而非大气温度进行计算会使T_m产生较大的误差,进而对该误差进行了不合理性分析.本文的分析将会对后续地基GNSS水汽反演研究中的处理提供一定的参考.      

Seasonal low-degree changes in terrestrial water mass load from global GNSS measurements

Large-scale mass redistribution in the terrestrial water storage (TWS) leads to changes in the low-degree spherical harmonic coefficients of the Earth’s surface mass density field. Studying these low-degree fluctuations is an important task that contributes to our understanding of continental hydrology. In this study, we use global GNSS measurements of vertical and horizontal crustal displacements that we correct for atmospheric and oceanic effects, and use a set of modified basis functions similar to Clarke et al. (Geophys J Int 171:1–10, 2007) to perform an inversion of the corrected measurements in order to recover changes in the coefficients of degree-0 (hydrological mass change), degree-1 (centre of mass shift) and degree-2 (flattening of the Earth) caused by variations in the TWS over the period January 2003–January 2015. We infer from the GNSS-derived degree-0 estimate an annual variation in total continental water mass with an amplitude of \((3.49 \pm 0.19) \times 10^{3}\) Gt and a phase of \(70^{\circ } \pm 3^{\circ }\) (implying a peak in early March), in excellent agreement with corresponding values derived from the Global Land Data Assimilation System (GLDAS) water storage model that amount to \((3.39 \pm 0.10) \times 10^{3}\) Gt and \(71^{\circ } \pm 2^{\circ }\), respectively. The degree-1 coefficients we recover from GNSS predict annual geocentre motion (i.e. the offset change between the centre of common mass and the centre of figure) caused by changes in TWS with amplitudes of \(0.69 \pm 0.07\) mm for GX, \(1.31 \pm 0.08\) mm for GY and \(2.60 \pm 0.13\) mm for GZ. These values agree with GLDAS and estimates obtained from the combination of GRACE and the output of an ocean model using the approach of Swenson et al. (J Geophys Res 113(B8), 2008) at the level of about 0.5, 0.3 and 0.9 mm for GX, GY and GZ, respectively. Corresponding degree-1 coefficients from SLR, however, generally show higher variability and predict larger amplitudes for GX and GZ. The results we obtain for the degree-2 coefficients from GNSS are slightly mixed, and the level of agreement with the other sources heavily depends on the individual coefficient being investigated. The best agreement is observed for \(T_{20}^C\) and \(T_{22}^S\), which contain the most prominent annual signals among the degree-2 coefficients, with amplitudes amounting to \((5.47 \pm 0.44) \times 10^{-3}\) and \((4.52 \pm 0.31) \times 10^{-3}\) m of equivalent water height (EWH), respectively, as inferred from GNSS. Corresponding agreement with values from SLR and GRACE is at the level of or better than \(0.4 \times 10^{-3}\) and \(0.9 \times 10^{-3}\) m of EWH for \(T_{20}^C\) and \(T_{22}^S\), respectively, while for both coefficients, GLDAS predicts smaller amplitudes. Somewhat lower agreement is obtained for the order-1 coefficients, \(T_{21}^C\) and \(T_{21}^S\), while our GNSS inversion seems unable to reliably recover \(T_{22}^C\). For all the coefficients we consider, the GNSS-derived estimates from the modified inversion approach are more consistent with the solutions from the other sources than corresponding estimates obtained from an unconstrained standard inversion.     

Evaluation of the impact of atmospheric pressure loading modeling on GNSS data analysis

In recent years, several studies have demonstrated the sensitivity of Global Navigation Satellite System (GNSS) station time series to displacements caused by atmospheric pressure loading (APL). Different methods to take the APL effect into account are used in these studies: applying the corrections from a geophysical model on weekly mean estimates of station coordinates, using observation-level corrections during data analysis, or solving for regression factors between the station displacement and the local pressure. The Center for Orbit Determination in Europe (CODE) is one of the global analysis centers of the International GNSS Service (IGS). The current quality of the IGS products urgently asks to consider this effect in the regular processing scheme. However, the resulting requirements for an APL model are demanding with respect to quality, latency, and—regarding the reprocessing activities—availability over a long time interval (at least from 1994 onward). The APL model of Petrov and Boy (J Geophys Res 109:B03405, 2004) is widely used within the VLBI community and is evaluated in this study with respect to these criteria. The reprocessing effort of CODE provides the basis for validating the APL model. The data set is used to solve for scaling factors for each station to evaluate the geophysical atmospheric non-tidal loading model. A consistent long-term validation of the model over 15 years, from 1994 to 2008, is thus possible. The time series of 15 years allows to study seasonal variations of the scaling factors using the dense GNSS tracking network of the IGS. By interpreting the scaling factors for the stations of the IGS network, the model by (2004) is shown to meet the expectations concerning the order of magnitude of the effect at individual stations within the uncertainty given by the GNSS data processing and within the limitations due to the model itself. The repeatability of station coordinates improves by 20% when applying the effect directly on the data analysis and by 10% when applying a post-processing correction to the resulting weekly coordinates compared with a solution without taking APL into account.     

动态GNSS监测瞬态无震蠕滑信号提取

断层的瞬态无震蠕滑常诱发震级高、破坏性强的地震。针对其滑动速度缓慢,难以探测的问题,本文基于GNSS连续坐标时间序列的异常波动提出一种断层瞬态无震蠕滑信息的自动探测方法。首先利用独立成分分析进行时空滤波,提高坐标时间序列的信噪比;然后计算坐标时间序列波动的相对强度指数及峰度值;最后通过累积分布函数将其转换为蠕滑信号概率,进而探测断层蠕滑事件。本文模拟500 d GNSS地表位移时间序列进行仿真试验,其中包含25 d瞬态蠕滑信号。试验结果表明,当信号强度至少与噪声水平相当时可有效探测出断层的蠕滑信息。计算Akutan地区连续3年的GNSS数据后探测到一个蠕滑信号,推断其可能为火山岩强烈运动引起的无震蠕滑。通过对四川省陆态网18个测站7年的GNSS数据处理后发现了4个异常信号。     

GNSS与GRACE联合的陆地水储量变化监测及其负荷形变研究

准确测定陆地水变化及其负荷形变,对揭示陆地水循环、维持高精度地球参考框架具有重要科学意义.结合GNSS非线性高程时序与环境负荷,可利用残余变化研究陆地水负荷.依据陆地水负荷运移与地球重力场之间物理机制,基于时变重力场对陆地水储量及其负荷形变定量监测.     

Android水平位移即时监测终端软件的实现

水平位移形变监测是轨道交通工程中十分重要的一环。随着移动互联技术和智能终端的发展及其在水平位移形变监测中的应用,监测的即时性及数据安全问题显得越来越重要。本文实现了一套基于Android设备的水平位移即时监测终端软件,通过Android设备的蓝牙、网络通信功能,将监测设备、Android设备、监测信息服务平台联系在一起,保证了监测的即时性;内嵌数据加密算法,保证了监测数据安全性。实际轨道交通工程中的应用表明:本文开发的终端软件实现了监测数据的实时采集和处理,提升了监测作业效率;实现了监测数据的加密,完善了工程安全机制。     

Benefits of receiver clock modeling in code-based GNSS navigation

Due to the limited frequency stability and poor accuracy of typical quartz oscillators built-in GNSS receivers, an additional receiver clock error has to be estimated in addition to the coordinates. This leads to several drawbacks especially in kinematic applications: At least four satellites in view are needed for navigation, high correlations between the clock estimates and the up-coordinates. This situation can be improved distinctly when connecting atomic clocks to GNSS receivers and modeling their behavior in a physically meaningful way (receiver clock modeling). Recent developments in miniaturizing atomic clocks result in so-called chip-scale atomic clocks and open up the possibility of using stable atomic clocks in GNSS navigation. We present two different methods of receiver clock modeling, namely in an extended Kalman filter and a sequential least-squares adjustment for code-based GNSS navigation using three different miniaturized atomic clocks. Using the data of several kinematic test drives, the benefits of clock modeling for GPS navigation solutions are assessed: decrease in the noise of the up-coordinates by up to 69 % to 20 cm level, decrease in minimal detectable biases by 16 %, and elimination of spikes and subsequently decrease in large position errors (35 %). Hence, a more robust position is obtained. Additionally, artificial partial satellite outages are generated to demonstrate position solutions with only three satellites in view.     

粒子群-BP神经网络模型在大坝变形监测中的应用

针对BP神经网络的初始化权值和阈值的随机性,易导致训练速度慢和落入局部极小等弱点,本文运用具有并行特性和全局优化能力的粒子群算法(PSO)对BP神经网络的权值和阈值进行优化,建立了基于粒子群-BP神经网络的大坝变形监测模型,并以丰满大坝多年监测的坝顶水平位移资料为例进行实证分析。与经典BP神经网络模型的预测结果相比,粒子群-BP神经网络模型的收敛速度更快、预测精度更高。     

全球导航卫星系统时间精度分析

全球导航卫星系统具有独立的时间参考系统,即GNSS时间,并溯源至UTC时间.GNSS时间精度参数不仅影响定位和授时的精度,也影响不同GNSS系统之间时间偏差的计算和预报,进而对GNSS系统的互操作性产生影响.本文对GNSS时间标度的产生和维持进行了分析,并通过分析给出了其精度估计结果.