GPS单历元定位新算法用于滑坡监测di

在滑坡变形较大时，常规GPS静态观测方式满足不了滑坡实时监测的需要. 本文结合滑坡变形的特点，利用GPS单历元定位新方法frac34;单历元阻尼LAMBDA方法，对滑坡实时形变进行了监测试验. 该方法不需要考虑GPS载波相位测量中棘手的周跳问题，每一历元即可搜索到正确的整周模糊度，从而获得监测点厘米级精度的坐标. 采用平滑方法后可以分辨出毫米级精度的坐标和滑动速度，扩大了GPS形变监测的应用范围. 本文简要介绍了新方法的原理，并使用低价位的单频GPS接收机，在江西省一个实测滑坡中取得了较好的应用效果.       

华北GPS地形变监测网的建立

全球定位系统GPS是利用监测地壳运动进行地震预测的新手段。华北是一个重要的地震监视区。应用GPS技术在华北布设了一个四维地形变监测网。由于采取了一系列先进的方法和措施,首次观测获得了高精度的成果,为今后大震预测和地球动力学的研究奠定了基础。     

GPS技术在上海市地面沉降研究中的应用

上海市自1998年开始开展了利用GPS技术监测地面沉降的研究,先后进行了可行性论证、GPS基准网建设、数据处理和平差方法的探索等一系列课题的研究,并布设了由34点组成的覆盖整个上海市的地面沉降监测基准网.为了进一步提高基准网的监测精度和沉降监测的时空分辨率,于2004年又布设了4个24小时连续运行的GPS固定站,对上海市地面沉降实施连续监测.为了考查GPS沉降监测的大地高可能达到的精度,研究合理的观测纲要与数据处理方案,本文在介绍并总结了上海市地面沉降GPS监测工作的同时,分析了GPS沉降监测基准网前后6期的观测数据.数据分析采用GAMIT/GLOBK软件包和GPS_NET软件,计算结果表明,在基线边长达到30～40km时,GPS在大地高方向上的中误差仍可控制在2mm左右,由此推算GPS监测地面沉降的分辨率在3mm左右,完全能够满足上海市地面沉降监测的需要.     

GPS在山区滑坡变形监测中的应用

山区滑坡的稳定性一直是倍受关注的一个重要安全问题。本文讨论了滑坡位移传统监测方法的缺陷和新型的基于GPS的自动监测技术所具有的独特优势,介绍了适用于山区公路滑坡位移监测的高精度的GPS技术。实践表明,采用GPS技术进行山区滑坡的稳定性监测,具有可操作性更强、精度更高、不受气候条件约束以及高度自动化等优点,能够为预测边坡可能出现失稳破坏的部位和变形破坏时间提供更可靠的监测资料,因而GPS监测技术值得在山区滑坡的变形监测领域中推广使用。     

甘肃天祝现代地壳运动GPS监测网的建立与第一期观测初步结果

本文以ＧＰＳ技术为基础，在位于青藏高原北缘、古浪－海原断裂带的甘肃天视一带布设了现代地壳运动监测网，并完成了第一期观测。初步处理结果表明，ＧＰＳ测量具有高精度，高效率等特点，可以成为监测现代地壳运动的最好方法之一。     

差分GPS在中国地磁监测网地理方位角测量中的应用

中国地磁监测网由地磁台站和流动地磁测点构成,实现对观测网内地磁场的相对记录和绝对观测。它的绝对观测采用世界上最先进的F、D、I测量仪器组合,其中地磁偏角D是地理北和地磁北的夹角。对地磁偏角D的绝对测量要求在地磁台站投入正常观测前必须完成观测墩和观测标志的地理方位角测量,流动地磁测点在每次地磁偏角D观测前后至少进行地理方位角测量各一次。据地磁监测网《观测规范》要求,地磁偏角D的观测精度必须≤6″,所以中国地磁监测网采用天文和差分GPS地理方位角两种测量方法,且两种方法观测精度相当,均≤6″。与天文测量相比,差分GPS观测不受天气等自然因素的限制,操作简单,观测快捷,数据处理计算机程序化。随着GPS技术的普及,中国地磁监测网差分GPS地理方位角测量方法已基本取代了天文方法。本文主要介绍差分GPS在中国地磁监测网地理方位角测量中的应用。     

GPS观测技术首次应用于工程稳定性监测取得可喜成果

新疆测绘集团于1991年11月引进美国Trimble 4000 SST双频GPS接收机后,1992年初组成GPS工作站(新疆维吾尔自治区地震局派三人参加),首先在拜城县克孜水库库坝区进行野外测量,先后取得两期成果。 克孜尔水库主坝位于秋里塔格断裂F_1断层和F_2断层之间,而副坝则跨在F_2断层之上,工程地质条件很差。为监测工程的稳定性,1991年8月布设常规监测网,此次GPS测量就是在常规网上进行的。     

首都圈GPS地形变监测网的布设与观测

介绍了首都圈ＧＰＳ地形变监测网的布设情况和提高观测精度的技术措施。     

利用GPS技术监测大坝表面变形

目前GPS技术已能够提供连续、实时、无障碍和自动化服务,被广泛应用于各类变形监测。本文探讨GPS技术在大坝变形监测中的应用,以某水库大坝为例,介绍水库大坝变形监测GPS点布设及观测要求,并提出GPS观测整体方案,通过案例介绍大坝变形监测点布设及数据处理流程,简单分析大坝变形及影响因素。     

小区域GPS形变监测网GAMIT数据处理结果与IGS站选取的关系探讨

利用GAMIT软件对北京市GPS形变监测网的2004年6月6日到6月12日的数据进行了处理,在处理过程中,对IGS站的选取分了4种情况:①选取13个IGS站;②选取10个IGS站;③选取6个IGS站;④选取1个IGS站。并对4种情况得出的结果从基线重复率WRMS、基线分量的偏差、标准化均方根差NRMS进行了比较,得出以下结论:对于100km内的小区域GPS形变监测网,使用GAMIT软件处理GPS观测数据,最好选取IGS站为区域网提供参考框架;同时对小区域的GPS形变监测网选取6个IGS站就比较合适,解算结果的精度可以达到监测地壳形变的要求;IGS站并不是数量上一定要多,但空间分布要尽量均匀,测站的近似坐标尽量准确。     

A new approach of single epoch GPS posi- tioning for landslide monitoring

Introduction China is a country with many landslides and debris flows. These disasters bring out a large amount of losses of life and property. It is significant to predict landslide incident by monitoring the deformations of these landslides. At past, triangulation and trilateration are traditional tools, but it is very difficult for them to realize real-time monitoring, and it is more dangerous for obser- vation workers when the deformation becomes larger. Because of many advantages such as…     

Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.     

Landslide activation behaviour illuminated by electrical resistance monitoring

A common factor in landslide activation (or reactivation) is subsurface moisture and associated pore pressure variations linked to rainfall. Monitoring of these subsurface hydrogeological processes is necessary to improve our understanding of water‐induced landslide activation. Geophysical approaches, electrical methods in particular, are increasingly being applied to landslide monitoring because they provide non‐invasive spatial information in heterogeneous subsurface environments that can be difficult to characterise using surface observations or intrusive sampling alone. Electrical techniques are sensitive to changing subsurface moisture conditions, and have proven to be a useful tool for investigating the hydrogeology of natural and engineered slopes. The objectives of this investigation were to further develop electrical resistance monitoring for slope stability assessment, and to validate the approach at an intermittently‐active UK landslide system to advance the understanding of complex landslide activation mechanisms. A long‐term transfer resistance dataset was collected from a grid of electrodes to allow spatial monitoring of the landslide. These data were interpreted using a synthesis of rainfall, temperature, GPS and piezometric records. The resistance data were corrected for seasonal temperature variations and electrode movements were monitored, as these processes were shown to mask moisture related changes. Results reveal that resistance monitoring is sensitive to soil moisture accumulation, including changes in piezometric levels, and can be used to study the principal activation mechanism of slow‐moving shallow earthflows. Spatial monitoring using resistance maps was shown to be particularly valuable as it revealed the evolution of subsurface moisture distribution, in the lead up to landslide activation. Key benefits of this approach are that it provides a simple, rapid and non‐invasive means of spatially monitoring subsurface moisture dynamics linked to landslide activation at high‐temporal resolution. Crucially, it provides a means of monitoring subsurface hydraulic changes in the build‐up to slope failure, thereby contributing to early warning of landslide events. Copyright © 2018 John Wiley & Sons, Ltd.     

Kinematics of landslide estimated by repeated GPS measurements in the Avcilar region of Istanbul, Turkey

The aim of this study is to analyze the spatial and temporal behavior of the landslide located in Avcilar region which is situated between Kucukcekmece and Buyukcekmece Lakes in the north-west of Marmara region, Turkey. A network consisting of 10 sites has been surveyed four times from November 2007 to May 2009 using Global Positioning System (GPS). The deformation analysis has been applied to determine the landslide movement parameters of the sites using GPS measurements of the four epochs. The reliable and high precision deformation rates are presented in terms of displacement vectors, velocity vectors and changes of accumulated strain. Landslides of the region are characterized from a regional GPS network. Each site has statistically different temporal behavior and significant relative motions and the region has irregular landslide movements.     

宽频带地震台网在滑坡地质灾害监测预报中的应用

在千将坪滑坡前地震动事件分析基础上,总结滑坡前滑坡体内部岩体形变和破裂所产生地震信号的典型特征,并建立宽频带地震台,对金坪子滑坡体和早谷田危岩体进行监测实验,提取基岩类、土质类和危岩体类3种基本物质组成滑坡的微振动前兆信息特征,为地震台网在滑坡监测预报中的应用推广和监测预报系统软件的研制奠定基础.根据测震学原理,研发滑坡监测分析系统软件（Smas）,可为滑坡大规模活动前快速预警.     

基于颗粒流方法的黄土地震滑坡数值模拟

基于颗粒流理论研究土质边坡动力稳定性及其滑动过程是近年来滑坡研究的一个新热点。在野外调查和室内试验的基础上,通过标定土体细观参数、模型建立、动力输入、动态监测等过程,利用PFC2D程序模拟了西吉县兴平乡堡湾村下马达子滑坡的失稳破坏运动过程,得到了该滑坡的破坏运动机理。得到如下结论:① 下马达子滑坡的失稳机制是在地震作用下斜坡前缘牵引、后缘推挤,使得坡肩受拉发生破坏,失稳后坡肩位置较大的速度和位移是地震滑坡破坏力强、致灾范围大的主要原因;② 黄土地震滑坡的滑坡后壁相对平缓,这是区别于重力滑坡的重要特征之一;③ 颗粒流模拟得到的滑坡前后相对高差和长度与实际情况较为吻合,因此,颗粒流方法可以用于地震滑坡滑距的预测。      

应用多传感器目标跟踪融合技术实现滑坡多点监测数据综合信息的提取

目前的滑坡预报模型只能利用一个关键监测点的监测数据，而不能同时利用多个监测点的监测信息，即只能处理一维时间序列问题.为了解决这个技术难题，本文将滑坡视为一个机动目标，将对滑坡的监测视为对机动目标的跟踪，提出了利用多传感器目标跟踪融合技术来处理滑坡多个点的监测数据的方法，分析了其可行性，并用实例说明了本文提出的方法在实际应用中是有效的.     

黄河流域刘家峡—兰州段滑坡灾害的InSAR识别及成因分析

我国滑坡灾害频发,尤其是西部地区,滑坡的隐蔽性强且危害巨大,对其灾害隐患进行早期识别对防灾减灾意义重大。传统的人工排查、大地测量等手段在山区难以开展且耗时耗力,合成孔径雷达干涉测量技术(InSAR)作为新兴的遥感测量手段,可以更精确、高效地进行大范围的滑坡灾害隐患识别。以黄河流域刘家峡-兰州段为研究区,采用永久散射体InSAR技术对覆盖该区域的111景Sentinel-1 C-SAR数据进行处理,并使用GPS数据进行趋势改正,获取研究区2014年10月—2019年12月间视距向形变场及形变特征,成功识别出1处位于永靖县的滑坡隐患区。该区的规模和量级均大于永靖县黑方台滑坡区,且未被前人提及,具有较大危险性。将InSAR结果与投影到视距向的GPS结果及前人结果进行比较,验证PS-InSAR方法的有效性;并结合历史强震资料、实地考察及时间序列对研究结果进行验证分析,结果表明该滑坡隐患区可能与1125年兰州M7.0地震和2017年九寨沟M_S7.0地震有关。本研究可为当地防灾减灾提供数据和技术支持。