GNSS在地表过程研究中的应用

王鹏, 刘静, 刘小利, 刘志军

王鹏, 刘静, 刘小利, 刘志军. GNSS在地表过程研究中的应用[J]. 武汉大学学报 ( 信息科学版), 2024, 49(12): 2159-2180. DOI: 10.13203/j.whugis20220113
引用本文: 王鹏, 刘静, 刘小利, 刘志军. GNSS在地表过程研究中的应用[J]. 武汉大学学报 ( 信息科学版), 2024, 49(12): 2159-2180. DOI: 10.13203/j.whugis20220113
WANG Peng, LIU Jing, LIU Xiaoli, LIU Zhijun. Application of GNSS in the Study of Earth Surface Processes[J]. Geomatics and Information Science of Wuhan University, 2024, 49(12): 2159-2180. DOI: 10.13203/j.whugis20220113
Citation: WANG Peng, LIU Jing, LIU Xiaoli, LIU Zhijun. Application of GNSS in the Study of Earth Surface Processes[J]. Geomatics and Information Science of Wuhan University, 2024, 49(12): 2159-2180. DOI: 10.13203/j.whugis20220113

GNSS在地表过程研究中的应用

基金项目: 

中国地震局地质研究所基本科研业务专项 IGCEA1812

地震动力学国家重点实验室项目 LED2022A03

国家自然科学基金 42030305

国家自然科学基金 4241001018

地震科技星火计划 XH22003C

详细信息
    作者简介:

    王鹏,博士,工程师,主要从事大地测量与地壳形变研究。wpeng0909@126.com

Application of GNSS in the Study of Earth Surface Processes

  • 摘要:

    地球表层系统是地球系统中与人类关系最密切的部分,以地表系统为研究对象的地表过程研究越来越重要。将地球物理和大地测量等手段创新性地运用于地表过程的研究逐渐成为一个新的学科交叉发展方向。全球导航卫星系统(global navigation satellite system, GNSS)观测技术因其具有高精度、全天候、大范围和准实时的特点,被广泛应用于地表过程研究中。从长期地壳形变、地震周期形变、大气可降水量、荷载响应、岩浆及火山活动、滑坡监测和反射测量等方面简要介绍了GNSS技术在地表过程研究中的应用现状,并对未来发展方向进行了讨论。研究表明,鉴于GNSS在观测技术方面的优势,应加强其在地表过程研究中的应用。

    Abstract:

    The earth surface system is the part of the earth system that is most closely related to human beings. The study of surface processes is becoming more and more important in earth system research. The innovative application of geophysics and geodesy to surface processes has gradually become a new interdisciplinary development direction. Global navigation satellite system (GNSS) observation technology is widely used in the study of surface processes because of its characteristics of high accuracy, all-weather, large range and quasi-real-time. In this paper, the application of GNSS technology in the study of surface processes is briefly introduced from the aspects of long-term crustal deformation, coseismic and post-seismic deformation, atmospheric precipitable water, load response, magma and volcanic activity, landslide monitoring and reflection measurement, and then the future development is discussed. The advantages of GNSS in observation technology highlight the importance of GNSS in the earth surface processes research.

  • 随着交通事业的蓬勃发展和“十四五”交通规划愿景的提出,中国桥梁建设进入高速成长期,在数量、规模、技术等方面跻身世界前列[1-2]。然而桥梁建造过程复杂繁琐,建造环境恶劣、地形地势复杂,易受风场、温度场、自身应力等内外环境的影响[3]。智能化是桥梁建设的重要方向,数字孪生是实现桥梁智能建造的有效途径。数字孪生将现实环境中桥梁的结构、状态和行为等映射到虚拟环境,为透彻理解和精准控制桥梁建造过程提供新手段[4-7]。由于桥梁建造工序和过程复杂多变,因此,如何高效地对桥梁建造过程涉及的对象、行为和状态进行准确描述与表达,是桥梁建造数字孪生构建亟需解决的关键问题[8]

    可视化能够直观而准确地描述桥梁信息。当前许多桥梁建造工程都基于建筑信息模型(building information modeling, BIM)技术对桥梁施工过程开发可视化平台[9-12],相较于施工图纸而言,其能更加明确地表示桥梁复杂的构件组成以及空间位置,从而为施工进度管理以及质量安全管控提供可靠的数据支撑。这些精细的模型能够用于展现桥梁施工工艺工法的动态过程,通过模拟展示施工工序,使得施工方案更加直观和可理解,从而保障施工的可靠性[13-16]。此外,对于桥梁建造环境也开展了较多可视化研究,如桥梁建造过程中的力学、风场、温度场等复杂环境下的三维可视化表达[17-20]。然而,上述研究主要侧重于聚焦数据管理与三维效果的可视化呈现,缺乏对于不同场域下桥梁属性、结构状态和行为的准确刻画,导致规范性差、认知效率低。

    叙事即通过一定媒介对发生在特定时间和空间中的事件进行再现。随着地图学制图理论、技术、方法的不断进步,将叙事与地理空间技术相结合,并将事件在空间参考框架中进行分析,可以进一步拓展空间叙事研究思路,促进地理信息和知识的传递[21]。2012年,国际制图协会艺术地图委员会邀请地图学、人文学等多领域专家对叙事与地图的关系进行了深入探讨[22]。同一时期,ESRI开发了地图、文本、多媒体等相结合的在线故事地图平台Story Maps,实现公众对个人地图故事的创建与编辑,叙事地图提供的故事叙述体系增强了地图和文本的表现力[23]。常见的地图叙事可视化方法包括流向图、时空立方体、动画等[24-26]。然而,以上可视化方法主要以二维表达为主,对于复杂性、模糊性、多样性事件描述能力不足导致认知效率低。考虑到一切时间皆发生于一定的空间内,将事件进一步映射至地理空间并建立多维可视化环境,可以进一步加深人员对于事件过程时空特征和规律的理解。

    知识图谱可以从多类型复杂数据中抽取实体和关系[27-29],有助于实现对桥梁建造知识的统一表达和关联管理,为桥梁建造过程时空叙事提供基础。然而目前对桥梁本体和知识图谱的研究较少,且侧重桥梁智能运维管理,主要针对桥梁结构、监测和检测信息进行数据的集成和检索[30-31],例如对桥梁混凝土构件裂缝灾害进行本体建模,实现裂缝位置、属性等信息的组织管理,并进行裂缝原因分析[32];又或者以桥梁本体为基础搭建桥梁检测问答系统,实现对桥梁的管理养护[33]。但是这些研究没有刻画复杂多变的桥梁建造过程,缺乏对桥梁建造对象、特征及相互关系的准确描述。

    针对以上问题,本文拟创新性地引入知识图谱和时空叙事技术,重点开展知识引导的桥梁建造过程时空叙事三维可视化方法研究。首先通过剖析桥梁建造对象特征和语义关系,建立“对象-行为-状态”三域关联的桥梁建造知识集成表达模型,利用知识图谱实现对桥梁建造知识的统一表达和关联管理;然后分析桥梁建造叙事要素,设计知识图谱中叙事要素的匹配规则,提出了桥梁叙述场景映射与实例化思路,给出时空叙事三维动态可视化方法,实现对桥梁建造过程复杂对象和关系的三维动态表达;最后研发原型系统并进行案例实验分析,以验证方法的有效性与可行性。

    桥梁建造过程时空叙事三维可视化方法的总体思路图如图1所示。首先,对桥梁建造事件的相关概念进行剖析,提出桥梁建造信息三域关联的集成表达模型,以实现三域关联的桥梁建造本体的构建,并基于本体为模板通过实体关系抽取构建桥梁建造知识图谱;然后,分析桥梁建造叙事要素特征,形成基于知识图谱实现叙事要素匹配方法与三维场景映射与实例化思路;最后,融合视觉变量、三维动画、图片和文字等多种叙事方法实现桥梁建造过程时空叙事三维动态可视化模拟,以生动直观表达桥梁建造过程复杂对象和时空关系。

    图  1  总体研究思路
    Figure  1.  Overall Research Ideas

    在地理环境复杂艰险的情况下,施工资源与条件需要有机协调,才能有序完成桥梁施工建造。本文剖析了桥梁建造事件需考虑的相关要素和数据,保证所构建知识图谱的完整性。同时,从施工技术变化的角度,提出桥梁建造信息“对象-行为-状态”关联的集成表达模型,按照事件推进、参与对象、状态演变对桥梁建造时间的对象分类和关联,以达成支持桥梁建造本体的构建。

    桥梁建造信息三域关联的集成表达模型如图2所示。行为域是推动桥梁施工建造过程变化的驱动力,建造过程中阶段变化伴随着要素、关系的时空变化。对象域是桥梁建造事件的主要参与者,是桥梁对象和资源分配的统一描述;状态域是要素时空与属性的语义变化表达。行为域、对象域、状态域三域联动改变。

    图  2  桥梁建造信息三域关联的集成表达模型
    Figure  2.  An Integrated Representation Model for the Association of Three Domains of Bridge Construction Information

    根据上述模型,本文通过以下结构来定义桥

    梁建造本体:

    BCO={EO,SO,DO|SRE|PRO} (1)

    其中,BCO(bridge construction ontology)代表桥梁建造事件本体;EO(event object)代表事件对象,包括桥梁建造的各施工流程;SO(scene object)代表桥梁场景对象;DO(data object)代表数据对象,表示事件中多样数据形式,包括桥梁构件模型数据、施工视频数据、文本资料等;SRE(semantic relation)代表语义关系,包含各对象内部间的关系;PRO(property)代表对象属性,如施工设备型号与类别、使用材料类型等。

    桥梁建造过程知识图谱先由模式层即本体结构引导下,建立节点与属性间的对应关系,即形成桥梁建造本体到知识图谱的映射关系。如图3所示,利用Neo4j,即知识图谱存储与可视化工具,进行节点的存储与桥梁建造过程知识图谱的可视化构建。

    图  3  桥梁建造知识图谱(部分)
    Figure  3.  Knowledge Graph for Bridge Construction Event (Part)

    基于桥梁建造事件的特征,提出桥梁建造事件的时空场景叙事要素。以桥梁建造的叙事场景为主干,桥梁建造事件的时空场景叙事要素为以下3类:①建造过程时间要素;②建造场景空间要素(数据、位置、姿态);③桥梁建造资源要素(施工设备、技术、材料等)。

    利用本文所构建知识图谱中节点与关系的规律性,如桥梁建造工序与施工所需的设备、材料、工法技术等节点相连的属性,从核心节点开始,通过定义图结构来实现精准高效的查询。如图4所示,利用关系节点匹配的方法,从桥梁建造知识图谱中提取叙事要素,达成清晰表达桥梁建造过程时空叙事的目标。图4中,V为节点,E为关系边。设H为节点的标签,K为关系的标签,则本文定义的图结构为:首先定义图G=(V,E,H,K)和图G1=(V1,E1,H1,K1),若其映射关系fVV1,同时符合以下条件:(1)∀aV,且H(a)=(f(a)),f(a)∈G1;(2)∀bE,且fb)∈E1;(3)∀cE,且Kc)=Kfc)),得到结论GG1子图同构。通过定义的子图结构与语义约束提取知识。图4中,左图定义结构能够匹配右图中3个相同结构(红色、蓝色和紫色虚线所示),然后对节点和关系定义特定约束,如节点类型与名称,最终得到唯一符合要求的子图(蓝色框线所示)。

    图  4  结构+语义的查询
    Figure  4.  Structure + Semantics Queries

    场景映射与实例化是指利用一定的技术与方法将真实地理场景转换为虚拟地理场景的过程,基于桥梁建造知识图谱查询的内容,能够实现基础地形、场景建筑和桥梁等对象精准地映射到三维场景中。如图5所示,本文桥梁建造场景映射步骤分为概念与逻辑模型设计、桥梁模型轻量化、物理模型建立和三维场景构建,实现桥梁建造三维场景的映射。

    图  5  桥梁建造场景映射与实例化
    Figure  5.  Bridge Construction Scene Mapping and Instantiation

    桥梁建造场景涉及的要素与信息庞杂,融合视觉变量、文字、图片和三维模型场景的叙事手段能够丰富场景内容,同时也造成认知困难。叙事地图通过划分场景视觉层次,对过程进行编排,以“讲故事”的形式提升学生视觉体验,为清晰明了、易于理解的可视化带来了新途径[34]。主题、时间、空间、过程是叙事地图的关键要素,主题决定故事的中心思想,是叙事的核心;时间和空间是故事发展的基础;过程是故事的具体内容。

    为增强数据信息表达,本文借鉴叙事地图思想,以桥梁建造为主题,以建造工序为故事线,以地形、影像和桥梁BIM模型为数据基础,采用文字、图片、三维动画、视觉变量相结合的手段,通过视角切换展示桥梁建造时空过程的动态变化。并且考虑到桥梁建造过程参与人员大多背景不同,信息理解能力存在差异,本文采用线性叙事结构为用户讲解桥梁建造的时空过程,如图6所示。

    图  6  桥梁建造过程时空叙事三维动态可视化
    Figure  6.  Bridge Construction Event Spatiotemporal Narrative 3D Dynamic Visualization

    本文将桥梁建造过程主要分为事件地点、施工背景及具体建造过程3个场景,每个场景都通过特定的视角切换展示,分别介绍事件发生地点、施工背景和具体建造过程。具体转场过程为:首先,从研究区的高空视角,通过地图、文字与符号等介绍桥梁工程的空间位置,将其作为叙事起始;其次,视角移至桥址位置,利用图文要素说明项目背景和施工部位基本信息;最后,依据施工工序关系,采用三维动画呈现建造过程,箭头标示施工方向,文本注明构件名称,同时图文并茂描述施工工序及机料法对象。

    本文研发的桥梁建造可视化平台的系统界面如图7所示,系统支持知识图谱查询和时空叙事的三维动态可视化展示。开发环境配置如下:AMD Ryzen 7 5800H with Radeon Graphics,

    图  7  系统界面
    Figure  7.  System Interface

    内存为16 GB,显卡显存为GeForce GTX 1650 2 032 MB;编译器为Visual Studio Code,第三方开源库为Cesium.js、Neovis.js,数据库为Neo4j。

    本文的实验数据分为模型、影像、文本三类。其中模型数据包括数字高程模型(digital elevation model,DEM)、BIM和倾斜摄影测量模型,DEM为案例区域增加了三维地形,BIM用于表达桥梁结构,倾斜摄影测量模型用于立体地描述区域内的地理对象。影像数据主要为文档对象模型(document object model,DOM),DOM能够为地形附着纹理,对道路等地表信息进行增强表达。文本数据为桥梁建造相关知识,包括桥梁结构、建造工序和施工资源等,这些数据被存储在Neo4j数据库中。

    本文构建的桥梁建造知识图谱明确了可视化实验所需的细节数据,可为桥梁建造的时空叙事三维动态表达提供准确的知识引导。以桥梁索塔建造为具体案例,展示了知识引导的桥梁建造过程三维动态可视化表达过程。

    索塔建造过程环节涵盖桩基、承台与塔座等施工环节。随着建造的进行,用户的视角会与施工部位相对应地调整。在桩基施工环节开始之前,首先利用文字描述桩基的基本属性,如数量和长度;其次,展示桩基施工动态模拟过程,旁边辅以文字描述和插图对施工工序进行详细说明,并对桩基模型进行标记说明;然后,是承台与塔座施工过程,先简要展示其基础参数,之后通过施工动画动态呈现施工过程,该过程中通过添加箭头符号标示建造方向,添加文字和插图为施工工序提供详细解读,此过程模拟如图8所示。

    图  8  索塔建造过程的三维动态可视化
    Figure  8.  3D Dynamic Visualization of Tower Construction Process

    为了评定本文方法传递场景信息的能力,从答题正确率和人员感受两方面对时空叙事三维动态可视化效果进行主客观评价。实验分为实验组与对照组,实验组即本文所提方法,对照组采用三维动画和文字的形式进行表达,文字为索塔建造相关信息,约350字,实验材料如图9所示。

    图  9  实验组和对照组形式
    Figure  9.  Experimental Group and Control Group

    实验随机召集了65名人员,其中实验组34名,对照组31名。两组人员完成实验后均立刻回答问卷问题,并且两组问卷内容相同。问卷包含7个问题,分为4个知识类问题和3个体验类问题,前者通过答题正确率客观评定用户对信息的理解程度,后者通过人员打分主观评价本文方法的可视化效果。问卷主要内容如表1所示。实验最后得到有效问卷共63份,其中实验组33份,对照组30份,有效率为97%,被筛选掉的部分问卷主要是由于多选或者漏选。

    表  1  问卷主要内容
    Table  1.  Main Content of the Questionnaire
    问题类型内容
    知识类Q1:大桥位于哪个省份?(四选一)
    Q2: 索塔建造顺序大致是怎样的?(四选一)
    Q3:液压爬模是在什么工序中被使用的?(四选一)
    Q4:索塔建造过程中涉及到的材料有哪些?(多选)
    体验类Q5: 材料清晰度如何?(非常好,较好,一般,较差,非常差)
    Q6:材料理解难度如何?(非常容易,较容易,一般,较难,非常难)
    Q7:材料丰富度如何?(非常丰富,较丰富,一般,不丰富,非常不丰富)
    下载: 导出CSV 
    | 显示表格

    本文对量表类问题即Q5~Q7从信度和效度两方面进行可靠性分析。信度用于评价问卷的一致性,通常认为克朗巴哈系数在0.8以上表示信度优秀,在0.6以下则需修订或删除;效度用于保证问卷的准确性和有效性,通常认为KMO(Kaiser-Meyer-Olkin)在0.7以上表示效度较好,Bartlett球形检验显著性小于0.05表示问卷结果符合要求。问卷结果如表2所示。从表2中可知问卷结果比较可靠,可进一步进行结果分析。

    表  2  问卷可靠性检验结果
    Table  2.  Questionnaire Reliability Test Results
    克朗巴哈系数KMOBartlett球形检验
    近似卡方自由度显著性
    0.9160.734203.5293<0.001
    下载: 导出CSV 
    | 显示表格

    实验结果通过问卷正确率及主观因素评价两个指标进行评价,主要的评价统计指标有平均值及方差,问卷平均分直观反映正确率,也就是参与人员认知效率的高低;方差反映实验数据离散程度。

    1)答题正确率

    通过Q1~Q4知识类问题的答题情况进行分析,首先对两组数据进行差异性检验,通过曼-惠特尼U检验得到p<0.001,远小于阈值0.05,说明两组数据差异明显。表3统计了每道题目的正确率(M)和方差(SD),统计结果表明,实验组答题情况普遍优于对照组,每题正确率均大于对照组,其中Q1的正确率提升0.05,Q2的正确率提升0.19,Q3的正确率提升0.16,Q4的正确率提升0.14。从整体答题正确率来看,实验组为76.5%,相比对照组的63.3%,正确率提升了13.2%。

    表  3  知识类问题得分统计结果
    Table  3.  Score Statistical Results of Knowledge-Based Questions
    问题实验组M±SD对照组M±SD
    Q10.88±0.330.83±0.37
    Q20.76±0.430.57±0.50
    Q30.79±0.410.63±0.48
    Q40.64±0.480.50±0.50
    下载: 导出CSV 
    | 显示表格

    人员个人答题情况如图10所示,从实验结果可以看出,实验组49%的人员能够答对全部题目,45%的人员能答对两题,6%的人员答对了一题,没有出现零正确率的情况;而对照组只有30%的人员能答对全部题目,40%的人员答对两题,15%的人员答对一题,有15%的人员为零正确率。总体而言,实验组90%以上的人至少能够答对两道题,效果远远优于对照组。

    图  10  两组答对题数占比
    Figure  10.  Percentage of Questions Answered Correctly by Both Groups

    2)人员感受

    人员感受是人员对材料难度、清晰度和丰富度的主观评价,共有5个等级可供选择,对应1~5分。对Q5~Q7的答题情况进行分析,首先对两组数据进行差异性检验,曼-惠特尼U检验得到p<0.001,证明两组数据存在显著差异。从体验类问题得分正确率的统计结果(表4)可以看出,实验组得分均高于4,而对照组得分均在3左右,其中Q5实验组得分较对照组高1.14,Q6实验组得分较对照组高1.12,Q7实验组得分较对照组高0.99。从整体平均得分来说,对照组得分3.07,实验组得分4.15,较对照组提升了1.08,说明人员对于实验组具有更高的认知效率。

    表  4  体验类问题得分统计结果
    Table  4.  Score Statistical Results for the ExperienceCategory Questions
    问题实验组M±SD对照组M±SD
    Q54.24±0.823.10±1.14
    Q64.09±0.832.97±1.22
    Q74.12±0.813.13±1.15
    下载: 导出CSV 
    | 显示表格

    图11统计了人员主观感受分数分布情况,从灰色到蓝色的色带分别代表非常差、较差、一般、较好、非常好5个等级。Q5统计了人员对材料清晰度评价,由图11可知,实验组45.46%和36.36%的人员分别认为材料非常清晰和较清晰,对照组则分别只有16.67%和13.33%,说明实验有效提升了材料的清晰度;Q6统计了人员对材料理解难易度评价,实验组36.37%和39.39%的人员分别认为材料理解起来非常容易和较容易,对照组则分别只有13.34%和20.00%,说明实验有效降低了材料的理解难度;Q7统计了人员对材料丰富度评价,实验组36.37%和42.42%的人员分别认为材料非常丰富和较丰富,对照组则分别只有13.33%和23.33%,说明实验有效提升了材料的丰富度。从清晰度、丰富度和理解难度综合的角度来看,实验组78.8%的人员具有良好认知体验,相对对照组的33.3%,提升了45.5%。

    图  11  体验类问题统计结果对比
    Figure  11.  Comparison of Statistical Results for Experience Category Questions

    3)结果原因分析

    以上结果的产生主要有两点原因:①实验组考虑了视觉变量,结合三维模型、图片、文字对桥梁建造过程进行了增强表达,较对照组而言表达方式更为丰富,更具有可读性;②实验组通过“讲故事”的方式表达桥梁建造过程中的地理要素,较对照组而言能够更加清楚地阐释时空变化过程,增强信息的传递能力。

    针对桥梁建造过程涉及对象众多、行为复杂、状态多变,导致表达存在规范性差、认知效率低等问题,本文提出了一种知识引导的桥梁建造过程时空叙事三维动态可视化方法。首先,建立了“对象-行为-状态”三域关联的集成表达模型和知识图谱,实现桥梁建造知识的统一表达和关联管理;然后,基于桥梁建造知识图谱匹配与叙事可视化相关联的场景要素,支持桥梁建造三维场景映射与实例化,并且设计了时空叙事三维动态可视化方法;最后研发了原型系统,开展桥梁建造过程三维动态可视化实验。实验结果表明,所提方法能够实现桥梁建造知识的准确表达和传递,达到提升人员对桥梁建造过程空间认知水平的效果。

    本文基于“对象-行为-状态”三域关联桥梁建造知识图谱,利用时空叙事对桥梁建造过程进行三维动态可视化表达,借助三维场景的可交互性及沉浸感等特点,虽然有效提升了桥梁建造过程认知的教育水平,但尚未考虑桥梁建造过程中结构数值仿真、施工方案预演及预测。就桥梁建造教育而言,结构数值仿真可以使学习者更准确地理解并掌握桥梁结构位移或应力等状态的变化趋势。而通过施工方案预演预测,可预先了解每种决策对应可能会引起的问题,并针对性地建立有效解决方案,培养问题决策能力。因此,如何将桥梁结构仿真与预测预案融入针对桥梁建造过程的教育内容中,将是未来非常重要的研究问题。

    http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20220113
  • 图  1   GNSS-IR测量原理示意图[205]

    Figure  1.   Principles of GNSS-IR[205]

  • [1] 丁永建, 周成虎, 邵明安, 等. 地表过程研究进展与趋势[J]. 地球科学进展, 2013, 28(4): 407-419.

    Ding Yongjian, Zhou Chenghu, Shao Ming’an, et al. Studies of Earth Surface Processes: Progress and Prospect[J]. Advances in Earth Science, 2013, 28(4): 407-419.

    [2] 丁永建, 张世强, 韩添丁, 等. 由地表过程向地表系统科学研究跨越的机遇与挑战[J]. 地球科学进展, 2014, 29(4): 443-455.

    Ding Yongjian, Zhang Shiqiang, Han Tianding, et al. Opportunities and Challenges of Studies Across Land Surface Processes to Land Surface System Sciences[J]. Advances in Earth Science, 2014, 29(4): 443-455.

    [3] 冷疏影, 宋长青. 陆地表层系统地理过程研究回顾与展望[J]. 地球科学进展, 2005, 20(6): 600-606.

    Leng Shuying, Song Changqing. Review of Land Surface Geographical Process Study and Prospects in China[J]. Advance in Earth Sciences, 2005, 20(6): 600-606.

    [4]

    Reid W V, Chen D, Goldfarb L, et al. Earth System Science for Global Sustainability: Grand Challenges[J]. Science, 2010, 330(6006): 916-917.

    [5] 刘静, 张金玉, 葛玉魁, 等. 构造地貌学: 构造-气候-地表过程相互作用的交叉研究[J]. 科学通报, 2018, 63(30): 3070-3088.

    Liu Jing, Zhang Jinyu, Ge Yukui, et al. Tectonic Geomorphology: An Interdisciplinary Study of the Interaction Among Tectonic Climatic and Surface Processes[J]. Chinese Science Bulletin, 2018, 63(30): 3070-3088.

    [6] 彭萍, 朱立平. 基于野外站网络的青藏高原地表过程观测研究[J]. 科技导报, 2017, 35(6): 97-102.

    Peng Ping, Zhu Liping. Observations of Land Surface Processes of the Tibetan Plateau Based on the Field Stations Network[J]. Science & Technology Review, 2017, 35(6): 97-102.

    [7] 徐自为, 刘绍民, 车涛, 等. 黑河流域地表过程综合观测网的运行、维护与数据质量控制[J]. 资源科学, 2020, 42(10): 1975-1986.

    Xu Ziwei, Liu Shaomin, Che Tao, et al. Operation and Maintenance and Data Quality Control of the Heihe Integrated Observatory Network[J]. Resources Science, 2020, 42(10): 1975-1986.

    [8]

    Bock Y, Melgar D. Physical Applications of GPS Geodesy: A Review[J]. Reports on Progress in Physics Physical Society (Great Britain), 2016, 79(10): 106801.

    [9] 宁津生, 姚宜斌, 张小红. 全球导航卫星系统发展综述[J]. 导航定位学报, 2013, 1(1): 3-8.

    Ning Jinsheng, Yao Yibin, Zhang Xiaohong. Review of the Development of Global Satellite Navigation System[J]. Journal of Navigation and Positioning, 2013, 1(1): 3-8.

    [10]

    Larson K M. Unanticipated Uses of the Global Positioning System[J]. Annual Review of Earth and Planetary Sciences, 2019, 47: 19-40.

    [11] 王敏, 沈正康. 中国大陆现今构造变形: 三十年的GPS观测与研究[J]. 中国地震, 2020, 36(4): 660-683.

    Wang Min, Shen Zhengkang. Present-Day Tectonic Deformation in Continental China: Thirty Years of GPS Observation and Research[J]. Earthquake Research in China, 2020, 36(4): 660-683.

    [12] 甘卫军, 李强, 张锐, 等. 中国大陆构造环境监测网络的建设与应用[J]. 工程研究-跨学科视野中的工程, 2012, 4(4): 324-331.

    Gan Weijun, Li Qiang, Zhang Rui, et al. Construction and Application of Tectonic and Environmental Observation Network of China’s Mainland[J]. Journal of Engineering Studies, 2012, 4(4): 324-331.

    [13]

    Herring T A, Melbourne T I, Murray M H, et al. Plate Boundary Observatory and Related Networks: GPS Data Analysis Methods and Geodetic Products[J]. Reviews of Geophysics, 2016, 54(4): 759-808.

    [14] 王坦, 李瑜, 张锐, 等. GPS在我国地震监测中的应用现状与发展展望[J]. 地震研究, 2021, 44(2): 192-207.

    Wang Tan, Li Yu, Zhang Rui, et al. GPS in Earthquake Monitoring in China: Current Situation and Prospect[J]. Journal of Seismological Research, 2021, 44(2): 192-207.

    [15]

    Sagiya T. A Decade of GEONET: 1994-2003 the Continuous GPS Observation in Japan and Its Impact on Earthquake Studies[J]. Earth, Planets and Space, 2004, 56(8): xxix-xli.

    [16]

    Shen Z K, King R W, Agnew D C, et al. A Unified Analysis of Crustal Motion in Southern California, 1970-2004: The SCEC Crustal Motion Map[J]. Journal of Geophysical Research: Solid Earth, 2011, 116(B11): B11402.

    [17]

    Thatcher W, Foulger G R, Julian B R, et al. Present-Day Deformation Across the Basin and Range Province, Western United States[J]. Science, 1999, 283(5408): 1714-1718.

    [18]

    Wdowinski S, Smith-Konter B, Bock Y, et al. Diffuse Interseismic Deformation Across the Pacific-North America Plate Boundary[J]. Geology, 2007, 35(4): 311.

    [19]

    Sagiya T, Miyazaki S, Tada T. Continuous GPS Array and Present-Day Crustal Deformation of Japan[J]. Pure and Applied Geophysics, 2000, 157(11): 2303-2322.

    [20] 牛之俊, 马宗晋, 陈鑫连, 等. 中国地壳运动观测网络[J]. 大地测量与地球动力学, 2002, 22(3): 88-93.

    Niu Zhijun, Ma Zongjin, Chen Xinlian, et al. Crustal Movement Observation Network of China[J]. Crustal Deformation and Earthquake, 2002, 22(3): 88-93.

    [21]

    Wang M, Shen Z K. Present-Day Crustal Deformation of Continental China Derived from GPS and Its Tectonic Implications[J]. Journal of Geophysical Research: Solid Earth, 2020, 125(2): e2019JB018774.

    [22]

    Wang Q, Zhang P Z, Freymueller J T, et al. Present-Day Crustal Deformation in China Constrained by Global Positioning System Measurements[J]. Science, 2001, 294(5542): 574-577.

    [23] 李强, 游新兆, 杨少敏, 等. 中国大陆构造变形高精度大密度GPS监测—现今速度场[J]. 中国科学: 地球科学, 2012, 42(5): 629-632.

    Li Qiang, You Xinzhao, Yang Shaomin, et al. High-Precision and High-Density GPS Monitoring of Structural Deformation in Chinese Mainland—Current Velocity Field[J]. Scientia Sinica (Terrae), 2012, 42(5): 629-632.

    [24]

    Zhang P Z, Shen Z K, Wang M, et al. Continuous Deformation of the Tibetan Plateau from Global Positioning System Data[J]. Geology, 2004, 32(9): 809.

    [25]

    Zhao B, Huang Y, Zhang C H, et al. Crustal Deformation on the Chinese Mainland During 1998-2014 Based on GPS Data[J]. Geodesy and Geodynamics, 2015, 6(1): 7-15.

    [26]

    Wang W, Qiao X J, Yang S M, et al. Present-Day Velocity Field and Block Kinematics of Tibetan Plateau from GPS Measurements[J]. Geophysical Journal International, 2017, 208(2): 1088-1102.

    [27]

    Zheng G, Wang H, Wright T J, et al. Crustal Deformation in the India-Eurasia Collision Zone from 25 Years of GPS Measurements[J]. Journal of Geophysical Research: Solid Earth, 2017, 122(11): 9290-9312.

    [28]

    Liang S M, Gan W J, Shen C Z, et al. Three-Dimensional Velocity Field of Present-Day Crustal Motion of the Tibetan Plateau Derived from GPS Measurements[J]. Journal of Geophysical Research: Solid Earth, 2013, 118(10): 5722-5732.

    [29]

    Rui X, Stamps D S. A Geodetic Strain Rate and Tectonic Velocity Model for China[J]. Geochemistry, Geophysics, Geosystems, 2019, 20(3): 1280-1297.

    [30] 王敏, 沈正康, 牛之俊, 等. 现今中国大陆地壳运动与活动块体模型[J]. 中国科学:地球科学, 2003, 33(S1): 21-32.

    Wang Min, Shen Zhengkang, Niu Zhijun, et al. Current Crustal Movement and Active Block Model in Chinese Mainland[J]. Scientia Sinica (Terrae), 2003, 33(S1): 21-32.

    [31]

    Shen Z K, Lü J N, Wang M, et al. Contemporary Crustal Deformation Around the Southeast Borderland of the Tibetan Plateau[J]. Journal of Geophysical Research: Solid Earth, 2005, 110(B11): B11409.

    [32]

    Meade B J. Present-Day Kinematics at the India-Asia Collision Zone[J]. Geology, 2007, 35(1): 81.

    [33]

    Thatcher W. Microplate Model for the Present-Day Deformation of Tibet[J]. Journal of Geophysical Research: Solid Earth, 2007, 112(B1): B01401.

    [34]

    Mao A L, Harrison C G A, Dixon T H. Noise in GPS Coordinate Time Series[J]. Journal of Geophysical Research: Solid Earth, 1999, 104(B2): 2797-2816.

    [35]

    Dong D, Fang P, Bock Y, et al. Anatomy of Apparent Seasonal Variations from GPS-Derived Site Position Time Series[J]. Journal of Geophysical Research: Solid Earth, 2002, 107(B4): 9-16.

    [36]

    Beavan J, Denys P, Denham M, et al. Distribution of Present-Day Vertical Deformation Across the Southern Alps, New Zealand, from 10 Years of GPS Data[J]. Geophysical Research Letters, 2010, 37(16): L16305.

    [37]

    Hao M, Freymueller J T, Wang Q L, et al. Vertical Crustal Movement Around the Southeastern Tibetan Plateau Constrained by GPS and GRACE Data[J]. Earth and Planetary Science Letters, 2016, 437: 1-8.

    [38] 赵斌, 聂兆生, 黄勇, 等. 大规模GPS揭示的华北地区现今垂直运动[J]. 大地测量与地球动力学, 2014, 34(5): 35-39.

    Zhao Bin, Nie Zhaosheng, Huang Yong, et al. Vertical Motion of North China Inferred from Dense GPS Neasurements[J]. Journal of Geodesy and Geodynamics, 2014, 34(5): 35-39.

    [39]

    Kleinherenbrink M, Riva R, Frederikse T. Acomparison of Methods to Estimate Vertical Land Motion Trends from GNSS and Altimetry at Tide Gauge Stations[J]. Ocean Science, 2018, 14(2): 187-204.

    [40]

    Hammond W C, Blewitt G, Kreemer C, et al. GPS Imaging of Global Vertical Land Motion for Studies of Sea Level Rise[J]. Journal of Geophysical Research: Solid Earth, 2021, 126(7): e2021JB022355.

    [41]

    Morton J Y T, van Diggelen F, Spilker J J, et al. Position, Navigation, and Timing Technologies in the 21st Century: Integrated Satellite Navigation, Sensor Systems, and Civil Applications[M]. New Jersey: John Wiley & Sons, 2021.

    [42]

    Baldi P, Casula G, Cenni N, et al. GPS-Based Monitoring of Land Subsidence in the Po Plain (Northern Italy)[J]. Earth and Planetary Science Letters, 2009, 288(1/2): 204-212.

    [43]

    Karegar M A, Dixon T H, Engelhart S E. Subsidence Along the Atlantic Coast of North America: Insights from GPS and Late Holocene Relative Sea Level Data[J]. Geophysical Research Letters, 2016, 43(7): 3126-3133.

    [44]

    Bürgmann R, Hilley G, Ferretti A, et al. Resolving Vertical Tectonics in the San Francisco Bay Area from Permanent Scatterer InSAR and GPS Analysis[J]. Geology, 2006, 34(3): 221.

    [45]

    Osmanoğlu B, Dixon T H, Wdowinski S, et al. Mexico City Subsidence Observed with Persistent Scatterer InSAR[J]. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(1): 1-12.

    [46]

    Du Z Y, Ge L L, Ng A H M, et al. Long-Term Subsidence in Mexico City from 2004 to 2018 Revealed by Five Synthetic Aperture Radar Sensors[J]. Land Degradation & Development, 2019, 30(15): 1785-1801.

    [47]

    Guo J M, Zhou L, Yao C L, et al. Surface Subsidence Analysis by Multi-temporal InSAR and GRACE: A Case Study in Beijing[J]. Sensors, 2016, 16(9): 1495.

    [48]

    Wang J, Howarth J D, McClymont E L, et al. Long-Term Patterns of Hillslope Erosion by Earthquake-Induced Landslides Shape Mountain Landscapes[J]. Science Advances, 2020, 6(23): eaaz6446.

    [49]

    Wang J, Jin Z D, Hilton R G, et al. Controls on Fluvial Evacuation of Sediment from Earthquake-Triggered Landslides[J]. Geology, 2015, 43(2): 115-118.

    [50]

    Wang J, Hilton R G, Jin Z D, et al. The Isotopic Composition and Fluxes of Particulate Organic Carbon Exported from the Eastern Margin of the Tibetan Plateau[J]. Geochimica et Cosmochimica Acta, 2019, 252: 1-15.

    [51]

    Wang J, Jin Z D, Hilton R G, et al. Earthquake-Triggered Increase in Biospheric Carbon Export from a Mountain Belt[J]. Geology, 2016, 44(6): 471-474.

    [52]

    Reid H F. The Mechanics of the Earthquake, the California Earthquake of April 18, 1906[J]. Carnegie Institute, Washington D C, 1910, 2: 3-56

    [53]

    Segall P, Davis J L. GPS Applications for Geodynamics and Earthquake Studies[J]. Annual Review of Earth and Planetary Sciences, 1997, 25: 301-336.

    [54]

    Scholz C H. The Mechanics of Earthquakes and Faulting[M]. Cambridge, UK: Cambridge University Press, 2018.

    [55]

    Harris R A. Large Earthquakes and Creeping Faults[J]. Reviews of Geophysics, 2017, 55(1): 169-198.

    [56]

    Savage J C, Burford R O. Geodetic Determination of Relative Plate Motion in Central California[J]. Journal of Geophysical Research, 1973, 78(5): 832-845.

    [57]

    Savage J C. A Dislocation Model of Strain Accumulation and Release at a Subduction Zone[J]. Journal of Geophysical Research: Solid Earth, 1983, 88(B6): 4984-4996.

    [58]

    McCaffrey R. Time-Dependent Inversion of Three-Component Continuous GPS for Steady and Transient Sources in Northern Cascadia[J]. Geophysical Research Letters, 2009, 36(7): L07304.

    [59]

    Jolivet R, Lasserre C, Doin M P, et al. Shallow Creep on the Haiyuan Fault (Gansu, China) Revealed by SAR Interferometry[J]. Journal of Geophysical Research: Solid Earth, 2012, 117(B6): B06401.

    [60]

    Li Y C, Nocquet J M, Shan X J, et al. Heterogeneous Interseismic Coupling Along the Xianshuihe-Xiaojiang Fault System, Eastern Tibet[J]. Journal of Geophysical Research: Solid Earth, 2021, 126(11): e2020JB021187.

    [61]

    Hirose H, Hirahara K, Kimata F, et al. A Slow Thrust Slip Event Following the Two 1996 Hyuganada Earthquakes Beneath the Bungo Channel, Southwest Japan[J]. Geophysical Research Letters, 1999, 26(21): 3237-3240.

    [62]

    Dragert G, Wang K, James T S. A Silent Slip Event on the Deeper Cascadia Subduction Interface[J]. Science, 2001, 292(5521): 1525-1528.

    [63]

    Miller M M, Melbourne T, Johnson D J, et al. Periodic Slow Earthquakes from the Cascadia Subduction Zone[J]. Science, 2002, 295(5564): 2423.

    [64]

    Ohta Y, Freymueller J T, Hreinsdóttir S, et al. A Large Slow Slip Event and the Depth of the Seismogenic Zone in the South Central Alaska Subduction Zone[J]. Earth and Planetary Science Letters, 2006, 247(1/2): 108-116.

    [65]

    Outerbridge K C, Dixon T H, Schwartz S Y, et al. A Tremor and Slip Event on the Cocos-Caribbean Subduction Zone as Measured by a Global Positioning System (GPS) and Seismic Network on the Nicoya Peninsula, Costa Rica[J]. Journal of Geophysical Research: Solid Earth, 2010, 115(B10): B10408.

    [66]

    Brown K M, Tryon M D, De Shon H R, et al. Correlated Transient Fluid Pulsing and Seismic Tremor in the Costa Rica Subduction Zone[J]. Earth and Planetary Science Letters, 2005, 238(1/2): 189-203.

    [67]

    Lowry A R. Resonant Slow Fault Slip in Subduction Zones Forced by Climatic Load Stress[J]. Nature, 2006, 442(7104): 802-805.

    [68]

    Shen Z K. Pole-Tide Modulation of Slow Slip Events at Circum-Pacific Subduction Zones[J]. Bulletin of the Seismological Society of America, 2005, 95(5): 2009-2015.

    [69]

    Zhao B, Bürgmann R, Wang D Z, et al. Aseismic Slip and Recent Ruptures of Persistent Asperities Along the Alaska-Aleutian Subduction Zone[J]. Nature Communications, 2022, 13(1): 3098.

    [70] 许才军, 王乐洋. 大地测量和地震数据联合反演地震震源破裂过程研究进展[J]. 武汉大学学报(信息科学版), 2010, 35(4): 457-462.

    Xu Caijun, Wang Leyang. Progress of Joint Inversion of Geodetic and Seismological Data for Seismic Source Rupture Process[J]. Geomatics and Information Science of Wuhan University, 2010, 35(4): 457-462.

    [71]

    Hartzell S, Mendoza C, Ramirez-Guzman L, et al. Rupture History of the 2008 Mw 7.9 Wenchuan, China, Earthquake: Evaluation of Separate and Joint Inversions of Geodetic, Teleseismic, and Strong-Motion Data[J]. Bulletin of the Seismological Society of America, 2013, 103(1): 353-370.

    [72]

    Wald D J, Heaton T H, Hudnut K W. The Slip History of the 1994 Northridge, California, Earthquake Determined from Strong-Motion, Teleseismic, GPS, and Leveling Data[J]. Bulletin of the Seismological Society of America, 1996, 86(1B): S49-S70.

    [73]

    Ma K F. Spatial and Temporal Distribution of Slip for the 1999 Chi-Chi, Taiwan, Earthquake[J]. Bulletin of the Seismological Society of America, 2004, 91(5): 1069-1087.

    [74] 许才军, 何平, 温扬茂, 等. 日本2011 Tohoku-Oki Mw 9.0级地震的同震形变及其滑动分布反演: GPS和InSAR约束[J]. 武汉大学学报(信息科学版), 2012, 37(12): 1387-1391.

    Xu Caijun, He Ping, Wen Yangmao, et al. Coseismic Deformation and Slip Distribution for 2011 Tohoku-Oki Mw 9.0 Earthquake: Constrained by GPS and InSAR[J]. Geomatics and Information Science of Wuhan University, 2012, 37(12): 1387-1391.

    [75]

    Shen Z K, Sun J B, Zhang P Z, et al. Slip Maxima at Fault Junctions and Rupturing of Barriers During the 2008 Wenchuan Earthquake[J]. Nature Geoscience, 2009, 2: 718-724.

    [76]

    Wang Q, Qiao X J, Lan Q G, et al. Rupture of Deep Faults in the 2008 Wenchuan Earthquake and Uplift of the Longmen Shan[J]. Nature Geoscience, 2011, 4: 634-640.

    [77]

    Wang M, Wang F, Jiang X, et al. GPS Determined Coseismic Slip of the 2021 Mw 7.4 Maduo, China, Earthquake and Its Tectonic Implication[J]. Geophysical Journal International, 2021, 228(3): 2048-2055.

    [78]

    Yu S B. Preseismic Deformation and Coseismic Displacements Associated with the 1999 Chi-Chi, Taiwan, Earthquake[J]. Bulletin of the Seismological Society of America, 2004, 91(5): 995-1012.

    [79]

    Larson K M, Bodin P, Gomberg J. Using 1-Hz GPS Data to Measure Deformations Caused by the Denali Fault Earthquake[J]. Science, 2003, 300(5624): 1421-1424.

    [80]

    Bock Y, Prawirodirdjo L, Melbourne T I. Detection of Arbitrarily Large Dynamic Ground Motions with a Dense High-Rate GPS Network[J]. Geophysical Research Letters, 2004, 31(6): L06604.

    [81]

    Ji C, Larson K M, Tan Y, et al. Slip History of the 2003 San Simeon Earthquake Constrained by Combining 1-Hz GPS, Strong Motion, and Teleseismic Data[J]. Geophysical Research Letters, 2004, 31(17): L17608.

    [82]

    Miyazaki S, Larson K M, Choi K, et al. Modeling the Rupture Process of the 2003 September 25 Tokachi-Oki (Hokkaido) Earthquake Using 1-Hz GPS Data[J]. Geophysical Research Letters, 2004, 31(21): L21603.

    [83] 殷海涛, 张培震, 甘卫军, 等. 高频GPS测定的汶川Ms 8.0级地震震时近场地表变形过程[J]. 科学通报, 2010, 55(26): 2621-2626.

    Yin Haitao, Zhang Peizhen, Gan Weijun, et al. Deformation Process of Near-Site Surface During Wenchuan Earthquake with Ms 8.0 Measured by High-Frequency GPS[J]. Chinese Science Bulletin, 2010, 55(26): 2621-2626.

    [84] 柴海山, 陈克杰, 魏国光, 等. 北斗三号与超高频GNSS同震形变监测: 以2021年青海玛多Mw 7.4地震为例[J]. 武汉大学学报(信息科学版), 2022, 47(6): 946-954.

    Chai Haishan, Chen Kejie, Wei Guoguang, et al. Coseismic Deformation Monitoring Using BDS-3 and Ultra-High Rate GNSS: A Case Study of the 2021 Maduo Mw 7.4 Earthquake[J]. Geomatics and Information Science of Wuhan University, 2022, 47(6): 946-954.

    [85]

    Chen K J, Avouac J P, Geng J H, et al. The 2021 Mw 7.4 Madoi Earthquake: An Archetype Bilateral Slip-Pulse Rupture Arrested at a Splay Fault[J]. Geophysical Research Letters, 2022, 49(2): e2021GL095243.

    [86]

    Delouis B, Nocquet J M, Vallée M. Slip Distribution of the February 27, 2010 Mw = 8.8 Maule Earthquake, Central Chile, from Static and High-Rate GPS, InSAR, and Broadband Teleseismic Data[J]. Geophysical Research Letters, 2010, 37(17): L17305.

    [87]

    Yue H, Lay T. Inversion of High-Rate (1 sps) GPS Data for Rupture Process of the 11 March 2011 Tohoku Earthquake (Mw 9.1)[J]. Geophysical Research Letters, 2011, 38(7): L00G09.

    [88]

    Galetzka J, Melgar D, Genrich J F, et al. Slip Pulse and Resonance of the Kathmandu Basin During the 2015 Gorkha Earthquake, Nepal[J]. Science, 2015, 349(6252): 1091-1095.

    [89]

    Vigny C, Socquet A, Peyrat S, et al. The 2010 Mw 8.8 Maule Megathrust Earthquake of Central Chile, Monitored by GPS[J]. Science, 2011, 332(6036): 1417-1421.

    [90]

    Smith S W, Wyss M. Displacement on the San Andreas Fault Subsequent to the 1966 Parkfield Earthquake[J]. Bulletin of the Seismological Society of America, 1968, 58(6): 1955-1973.

    [91] 郭汝梦, 杨浩哲, 汤雄伟, 等. 卫星大地测量成像地震周期形变研究综述[J]. 武汉大学学报(信息科学版), 2022, 47(6): 799-806.

    Guo Rumeng, Yang Haozhe, Tang Xiongwei, et al. A Review on Satellite Geodesy Applied to Image the Earthquake Cycle Deformation[J]. Geomatics and Information Science of Wuhan University, 2022, 47(6): 799-806.

    [92]

    Nur A, Mavko G. Postseismic Viscoelastic Rebound[J]. Science, 1974, 183(4121): 204-206.

    [93]

    Lienkaemper J J, DeLong S B, Domrose C J, et al. Afterslip Behavior Following the 2014 M 6.0 South Napa Earthquake with Implications for Afterslip Forecasting on Other Seismogenic Faults[J]. Seismological Research Letters, 2016, 87(3): 609-619.

    [94]

    Guo R M, Zheng Y, Xu J Q, et al. Seismic and Aseismic Fault Slip Associated with the 2017 Mw 8.2 Chiapas, Mexico, Earthquake Sequence[J]. Seismological Research Letters, 2019, 90(3): 1111-1120.

    [95]

    Liu-Zeng J, Zhang Z, Rollins C, et al. Postseismic Deformation Following the 2015 Mw 7.8 Gorkha (Nepal) Earthquake: New GPS Data, Kinematic and Dynamic Models, and the Roles of Afterslip and Viscoelastic Relaxation[J]. Journal of Geophysical Research: Solid Earth, 2020, 125(9): e2020JB019852.

    [96]

    Liu K, Geng J H, Wen Y M, et al. Very Early Postseismic Deformation Following the 2015 Mw 8.3 Illapel Earthquake, Chile Revealed from Kinematic GPS[J]. Geophysical Research Letters, 2022, 49(11): e2022GL098526.

    [97]

    Peltzer G, Rosen P, Rogez F, et al. Postseismic Rebound in Fault Step-Overs Caused by Pore Fluid Flow[J]. Science, 1996, 273(5279): 1202-1204.

    [98]

    Jónsson S, Segall P, Pedersen R, et al. Post-Earthquake Ground Movements Correlated to Pore-Pressure Transients[J]. Nature, 2003, 424(6945): 179-183.

    [99]

    Peltzer G, Rosen P, Rogez F, et al. Poroelastic Rebound Along the Landers 1992 Earthquake Surface Rupture[J]. Journal of Geophysical Research: Solid Earth, 1998, 103(B12): 30131-30145.

    [100]

    Hu Y, Bürgmann R, Freymueller J T, et al. Contributions of Poroelastic Rebound and a Weak Volcanic Arc to the Postseismic Deformation of the 2011 Tohoku Earthquake[J]. Earth, Planets and Space, 2014, 66(1): 106.

    [101]

    Yang H Z, Guo R M, Zhou J C, et al. Transient Poroelastic Response to Megathrust Earthquakes: A Look at the 2015 Mw 8.3 Illapel, Chile, Event[J]. Geophysical Journal International, 2022, 230(2): 908-915.

    [102]

    Panuntun H, Miyazaki S, Fukuda Y, et al. Probing the Poisson’s Ratio of Poroelastic Rebound Following the 2011 Mw 9.0 Tohoku Earthquake[J]. Geophysical Journal International, 2018, 215(3): 2206-2221.

    [103]

    McCormack K, Hesse M A, Dixon T, et al. Modeling the Contribution of Poroelastic Deformation to Postseismic Geodetic Signals[J]. Geophysical Research Letters, 2020, 47(8): e2020GL086945.

    [104]

    Bürgmann R, Dresen G. Rheology of the Lower Crust and Upper Mantle: Evidence from Rock Mechanics, Geodesy, and Field Observations[J]. Annual Review of Earth and Planetary Sciences, 2008, 36: 531-567.

    [105]

    Sun T, Wang K L, Iinuma T, et al. Prevalence of Viscoelastic Relaxation After the 2011 Tohoku-Oki Earthquake[J]. Nature, 2014, 514(7520): 84-87.

    [106]

    Zhao B, Bürgmann R, Wang D Z, et al. Dominant Controls of Downdip Afterslip and Viscous Relaxation on the Postseismic Displacements Following the Mw 7.9 Gorkha, Nepal, Earthquake[J]. Journal of Geophysical Research: Solid Earth, 2017, 122(10): 8376-8401.

    [107]

    Huang M H, Bürgmann R, Freed A M. Probing the Lithospheric Rheology Across the Eastern Margin of the Tibetan Plateau[J]. Earth and Planetary Science Letters, 2014, 396: 88-96.

    [108]

    Tian Z, Freymueller J T, Yang Z Q. Spatio-Temporal Variations of Afterslip and Viscoelastic Relaxation Following the Mw 7.8 Gorkha (Nepal) Earthquake[J]. Earth and Planetary Science Letters, 2020, 532: 116031.

    [109]

    Wang M, Shen Z K, Wang Y Z, et al. Postseismic Deformation of the 2008 Wenchuan Earthquake Illuminates Lithospheric Rheological Structure and Dynamics of Eastern Tibet[J]. Journal of Geophysical Research: Solid Earth, 2021, 126(9): e2021JB022399.

    [110]

    Kiehl J T, Trenberth K E. Earth’s Annual Global Mean Energy Budget[J]. Bulletin of the American Meteorological Society, 1997, 78(2): 197-208.

    [111]

    Bevis M, Businger S, Chiswell S, et al. GPS Me-teorology: Mapping Zenith Wet Delays Onto Precipitable Water[J]. Journal of Applied Meteorology, 1994, 33(3): 379-386.

    [112]

    Bevis M, Businger S, Herring T A, et al. GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System[J]. Journal of Geophysical Research: Atmospheres, 1992, 97(D14): 15787-15801.

    [113]

    Rocken C, Hove T V, Johnson J, et al. GPS/STORM—GPS Sensing of Atmospheric Water Vapor for Meteorology[J]. Journal of Atmospheric and Oceanic Technology, 1995, 12(3): 468-478.

    [114]

    Rocken C, Ware R, Van Hove T, et al. Sensing Atmospheric Water Vapor with the Global Positioning System[J]. Geophysical Research Letters, 1993, 20(23): 2631-2634.

    [115]

    Duan J P, Bevis M, Fang P, et al. GPS Meteorology: Direct Estimation of the Absolute Value of Precipitable Water[J]. Journal of Applied Meteorology, 1996, 35(6): 830-838.

    [116]

    Flores A, Ruffini G, Rius A. 4D Tropospheric Tomography Using GPS Slant Wet Delays[J]. Annales Geophysicae, 2000, 18(2): 223-234.

    [117]

    Haji Aghajany S, Amerian Y. Three Dimensional Ray Tracing Technique for Tropospheric Water Vapor Tomography Using GPS Measurements[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2017, 164: 81-88.

    [118]

    Braun J, Rocken C, Liljegren J. Comparisons of Line-of-Sight Water Vapor Observations Using the Global Positioning System and a Pointing Microwave Radiometer[J]. Journal of Atmospheric and Oceanic Technology, 2003, 20(5): 606-612.

    [119]

    Wang Y C, Ding N, Zhang Y, et al. A New Approach of the Global Navigation Satellite System Tomography for any Size of GNSS Network[J]. Remote Sensing, 2020, 12(4): 617.

    [120] 刘敏, 郭鹏, 叶其欣, 等. 上海地区地基GPS水汽三维层析技术和初步应用[J]. 天文学报, 2010, 51(3): 299-308.

    Liu Min, Guo Peng, Ye Qixin, et al. The 3D Tomography Technique and Application of Water Vapor Using Ground-Based GPS Networks in Shanghai[J]. Acta Astronomica Sinica, 2010, 51(3): 299-308.

    [121] 张双成, 刘经南, 叶世榕, 等. 顾及双差残差反演GPS信号方向的斜路径水汽含量[J]. 武汉大学学报(信息科学版), 2009, 34(1): 100-104.

    Zhang Shuangcheng, Liu Jingnan, Ye Shirong, et al. Retrieval of Water Vapor Along the GPS Slant Path Based on Double-Differenced Residuals[J]. Geomatics and Information Science of Wuhan University, 2009, 34(1): 100-104.

    [122]

    Landskron D, Böhm J. VMF3/GPT3: Refined Discrete and Empirical Troposphere Mapping Functions[J]. Journal of Geodesy, 2018, 92(4): 349-360.

    [123]

    Leandro R, Santos M, Langley R. UNB Neutral Atmosphere Models: Development and Performance [C]//National Technical Meeting of the Institute of Navigation, California, USA, 2006.

    [124]

    Li W, Yuan Y B, Ou J K, et al. A New Global Zenith Tropospheric Delay Model IGGtrop for GNSS Applications[J]. Chinese Science Bulletin, 2012, 57(17): 2132-2139.

    [125]

    Boehm J, Niell A, Tregoning P, et al. Global Mapping Function (GMF): A New Empirical Mapping Function Based on Numerical Weather Model Data[J]. Geophysical Research Letters, 2006, 33(7): L07304.

    [126]

    Boehm J, Schuh H. Vienna Mapping Functions in VLBI Analyses[J]. Geophysical Research Letters, 2004, 31(1): L01603.

    [127]

    Boehm J, Werl B, Schuh H. Troposphere Mapping Functions for GPS and Very Long Baseline Interfero-metry from European Centre for Medium-Range Weather Forecasts Operational Analysis Data[J]. Journal of Geophysical Research: Solid Earth, 2006, 111(B2): B02406.

    [128]

    Ding M H. A Second Generation of the Neural Network Model for Predicting Weighted Mean Temperature[J]. GPS Solutions, 2020, 24(2): 61.

    [129]

    Huang L K, Liu L L, Chen H, et al. An Improved Atmospheric Weighted Mean Temperature Model and Its Impact on GNSS Precipitable Water Vapor Estimates for China[J]. GPS Solutions, 2019, 23(2): 51.

    [130]

    Ding M H. A Neural Network Model for Predicting Weighted Mean Temperature[J]. Journal of Geodesy, 2018, 92(10): 1187-1198.

    [131]

    Yao Y B, Zhu S, Yue S Q. A Globally Applicable, Season-Specific Model for Estimating the Weighted Mean Temperature of the Atmosphere[J]. Journal of Geodesy, 2012, 86(12): 1125-1135.

    [132]

    Wang J H, Zhang L Y, Dai A G. Global Estimates of Water-Vapor-Weighted Mean Temperature of the Atmosphere for GPS Applications[J]. Journal of Geophysical Research: Atmospheres, 2005, 110(D21): e2005jd006215.

    [133] 曲建光, 刘基余, 韩中元. 利用天顶对流层延迟数据直接推算水汽含量的研究[J]. 武汉大学学报(信息科学版), 2005, 30(7): 625-628.

    Qu Jianguang, Liu Jiyu, Han Zhongyuan. Research on the Calculating Directly Water Vapor Value Using Zenith Tropospheric Delay Data[J]. Geomatics and Information Science of Wuhan University, 2005, 30(7): 625-628.

    [134] 王勇, 刘严萍, 柳林涛, 等. 区域GPS网对流层延迟直接推算可降水量研究[J]. 热带气象学报, 2007, 23(5): 510-514.

    Wang Yong, Liu Yanping, Liu Lintao, et al. The Study of Directly Calculating Precipitable Water Vapor with Zenith Tropospheric Delay of GPS Network[J]. Journal of Tropical Meteorology, 2007, 23(5): 510-514.

    [135] 王勇, 刘严萍, 柳林涛, 等. 无气象要素的GPS对流层延迟推算可降水量的研究[J]. 测绘科学, 2007, 32(3): 122-124.

    Wang Yong, Liu Yanping, Liu Lintao, et al. The Study of Calculating Precipitable Water Vapor Using GPS Zenith Tropospheric Delay Without Meteorological Data[J]. Science of Surveying and Mapping, 2007, 32(3): 122-124.

    [136] 王勇, 柳林涛, 郝晓光, 等. 武汉地区GPS气象网应用研究[J]. 测绘学报, 2007, 36(2): 141-145.

    Wang Yong, Liu Lintao, Hao Xiaoguang, et al. The Application Study of the GPS Meteorology Network in Wuhan Region[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(2): 141-145.

    [137] 易正晖, 王帅民, 王勇, 等. GNSS对流层延迟推算可降水量的季节转换模型研究[J]. 大地测量与地球动力学, 2017, 37(8): 830-834.

    Yi Zhenghui, Wang Shuaimin, Wang Yong, et al. Research on Seasonal Transition Model of GNSS Zenith Tropospheric Delay Calculating Precipitable Water Vapor[J]. Journal of Geodesy and Geodynamics, 2017, 37(8): 830-834.

    [138]

    Hagemann S, Bengtsson L, Gendt G. On the Determination of Atmospheric Water Vapor from GPS Measurements[J]. Journal of Geophysical Research: Atmospheres, 2003, 108(D21): e2002jd003235.

    [139]

    Wang Y, Yang K, Pan Z Y, et al. Evaluation of Precipitable Water Vapor from Four Satellite Products and Four Reanalysis Datasets Against GPS Measurements on the Southern Tibetan Plateau[J]. Journal of Climate, 2017, 30(15): 5699-5713.

    [140]

    Zhang W X, Lou Y D, Huang J F, et al. Multiscale Variations of Precipitable Water over China Based on 1999-2015 Ground-Based GPS Observations and Evaluations of Reanalysis Products[J]. Journal of Climate, 2018, 31(3): 945-962.

    [141]

    Zhang Y L, Cai C S, Chen B Y, et al. Consistency Evaluation of Precipitable Water Vapor Derived from ERA5, ERA-Interim, GNSS, and Radiosondes over China[J]. Radio Science, 2019, 54(7): 561-571.

    [142]

    Xiong Z H, Sang J Z, Sun X G, et al. Comparisons of Performance Using Data Assimilation and Data Fusion Approaches in Acquiring Precipitable Water Vapor: A Case Study of a Western United States of America Area[J]. Water, 2020, 12(10): 2943.

    [143]

    Zhang B, Yao Y B, Xin L Y, et al. Precipitable Water Vapor Fusion: An Approach Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation[J]. Journal of Geodesy, 2019, 93(12): 2605-2620.

    [144]

    Zhao Q Z, Du Z, Yao W Q, et al. Hybrid Precipitable Water Vapor Fusion Model in China[J]. Journal of Atmospheric and Solar⁃Terrestrial Physics, 2020, 208: 105387.

    [145]

    van Dam T M, Herring T A. Detection of Atmospheric Pressure Loading Using Very Long Baseline Interferometry Measurements[J]. Journal of Geophysical Research: Solid Earth, 1994, 99(B3): 4505-4517.

    [146]

    van Dam T M, Wahr J, Chao Y, et al. Predictions of Crustal Deformation and of Geoid and Sea-Level Variability Caused by Oceanic and Atmospheric Loading[J]. Geophysical Journal International, 1997, 129(3): 507-517.

    [147] 龚国栋, 花向红, 贺小星, 等. GPS坐标时间序列中地表环境负载效应区域特征分析[J]. 大地测量与地球动力学, 2017, 37(9): 961-967.

    Gong Guodong, Hua Xianghong, He Xiaoxing, et al. Analysis of Regional Characteristics of Environment Load Effect in GPS Coordinate Time Series[J]. Journal of Geodesy and Geodynamics, 2017, 37(9): 961-967.

    [148]

    Dam T V, Collilieux X, Wuite J, et al. Nontidal Ocean Loading: Amplitudes and Potential Effects in GPS Height Time Series[J]. Journal of Geodesy, 2012, 86(11): 1043-1057.

    [149] 刘经南, 张化疑, 刘焱雄, 等. GNSS研究海潮负荷效应进展[J]. 武汉大学学报(信息科学版), 2016, 41(1): 9-14.

    Liu Jingnan, Zhang Huayi, Liu Yanxiong, et al. Progress of Ocean Tide Loading Inversion Based on GNSS[J]. Geomatics and Information Science of Wuhan University, 2016, 41(1): 9-14.

    [150]

    Dach R, Böhm J, Lutz S, et al. Evaluation of the Impact of Atmospheric Pressure Loading Modeling on GNSS Data Analysis[J]. Journal of Geodesy, 2011, 85(2): 75-91.

    [151]

    Tregoning P, van Dam T M. Atmospheric Pressure Loading Corrections Applied to GPS Data at the Observation Level[J]. Geophysical Research Letters, 2005, 32(22): L22310.

    [152]

    Tregoning P, van Dam T M. Effects of Atmospheric Pressure Loading and Seven-Parameter Transformations on Estimates of Geocenter Motion and Station Heights from Space Geodetic Observations[J]. Journal of Geophysical Research: Solid Earth, 2005, 110(B3): B03408.

    [153]

    Liu L, Khan S A, van Dam T M, et al. Annual Variations in GPS-Measured Vertical Displacements near Upernavik Isstrøm (Greenland) and Contributions from Surface Mass Loading[J]. Journal of Geophysical Research: Solid Earth, 2017, 122(1): 677-691.

    [154]

    Williams S D P, Penna N T. Non-tidal Ocean Loading Effects on Geodetic GPS Heights[J]. Geophysical Research Letters, 2011, 38(9): L09314.

    [155]

    Martens H R, Argus D F, Norberg C, et al. Atmospheric Pressure Loading in GPS Positions: Dependency on GPS Processing Methods and Effect on Assessment of Seasonal Deformation in the Contiguous USA and Alaska[J]. Journal of Geodesy, 2020, 94(12): 115.

    [156] 陆桂华, 何海. 全球水循环研究进展[J]. 水科学进展, 2006, 17(3): 419-424.

    Lu Guihua, He Hai. View of Global Hydrological Cycle[J]. Advances in Water Science, 2006, 17(3): 419-424.

    [157]

    Bettinelli P, Avouac J P, Flouzat M, et al. Seasonal Variations of Seismicity and Geodetic Strain in the Himalaya Induced by Surface Hydrology[J]. Earth and Planetary Science Letters, 2008, 266(3/4): 332-344.

    [158]

    Argus D F, Fu Y N, Landerer F W. Seasonal Variation in Total Water Storage in California Inferred from GPS Observations of Vertical Land Motion[J]. Geophysical Research Letters, 2014, 41(6): 1971-1980.

    [159]

    Borsa A A, Agnew D C, Cayan D R. Ongoing Drought-Induced Uplift in the Western United States[J]. Science, 2014, 345(6204): 1587-1590.

    [160]

    Argus D F, Landerer F W, Wiese D N, et al. Sustained Water Loss in California’s Mountain Ranges During Severe Drought from 2012 to 2015 Inferred from GPS[J]. Journal of Geophysical Research: Solid Earth, 2017, 122(12): 10559-10585.

    [161]

    Jiang W P, Yuan P, Chen H, et al. Annual Variations of Monsoon and Drought Detected by GPS: A Case Study in Yunnan, China[J]. Scientific Reports, 2017, 7(1): 5874.

    [162]

    Zhan W, Li F, Hao W F, et al. Regional Characte-ristics and Influencing Factors of Seasonal Vertical Crustal Motions in Yunnan, China[J]. Geophysical Journal International, 2017, 210(3): 1295-1304.

    [163] 盛传贞, 甘卫军, 梁诗明, 等. 滇西地区GPS时间序列中陆地水载荷形变干扰的GRACE分辨与剔除[J]. 地球物理学报, 2014, 57(1): 42-52.

    Sheng Chuanzhen, Gan Weijun, Liang Shiming, et al. Identification and Elimination of Non-tectonic Crustal Deformation Caused by Land Water from GPS Time Series in the Western Yunnan Province Based on GRACE Observations[J]. Chinese Journal of Geophysics, 2014, 57(1): 42-52.

    [164] 丁一航, 黄丁发, 师悦龄, 等. 利用GPS和GRACE分析四川地表垂向位移变化[J]. 地球物理学报, 2018, 61(12): 4777-4788.

    Ding Yihang, Huang Dingfa, Shi Yueling, et al. Determination of Vertical Surface Displacements in Sichuan Using GPS and GRACE Measurements[J]. Chinese Journal of Geophysics, 2018, 61(12): 4777-4788.

    [165] 胡顺强, 王坦, 管雅慧, 等. 利用GPS和水文负载模型研究云南地区垂向季节性波动变化和构造变形[J]. 地球物理学报, 2021, 64(8): 2613-2630.

    Hu Shunqiang, Wang Tan, Guan Yahui, et al. Analyzing the Seasonal Fluctuation and Vertical Deformation in Yunnan Province Based on GPS Measurement and Hydrological Loading Model[J]. Chinese Journal of Geophysics, 2021, 64(8): 2613-2630.

    [166]

    Borsa A A, Mencin D, van Dam T M. The Weight of a Storm: What Observations of Earth Surface Deformation Can Tell Us About Hurricane Harvey[J].AGU Fall Meeting Abstracts, 2017, 23: 2872.

    [167] 汪汉胜, Wu Patrick, 许厚泽. 冰川均衡调整(GIA)的研究[J]. 地球物理学进展, 2009, 24(6): 1958-1967.

    Wang Hansheng, Wu Patrick, Xu Houze. A Review of Research in Glacial Isostatic Adjustment[J]. Progress in Geophysics, 2009, 24(6): 1958-1967.

    [168]

    Jiang Y, Dixon T H, Wdowinski S. Accelerating Uplift in the North Atlantic Region as an Indicator of Ice Loss[J]. Nature Geoscience, 2010, 3: 404-407.

    [169]

    Khan S A, Sasgen I, Bevis M, et al. Geodetic Measurements Reveal Similarities Between Post–Last Glacial Maximum and Present-Day Mass Loss from the Greenland Ice Sheet[J]. Science Advances, 2016, 2(9): e1600931.

    [170]

    Bevis M, Wahr J, Khan S A, et al. Bedrock Displacements in Greenland Manifest Ice Mass Variations, Climate Cycles and Climate Change[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(30): 11944-11948.

    [171]

    Larsen C F, Motyka R J, Freymueller J T, et al. Rapid Viscoelastic Uplift in Southeast Alaska Caused by Post-Little Ice Age Glacial Retreat[J]. Earth and Planetary Science Letters, 2005, 237(3/4): 548-560.

    [172]

    Elliott J L, Larsen C F, Freymueller J T, et al. Tectonic Block Motion and Glacial Isostatic Adjustment in Southeast Alaska and Adjacent Canada Constrained by GPS Measurements[J]. Journal of Geophysical Research: Solid Earth, 2010, 115(B9): B09407.

    [173]

    Hu Y, Freymueller J T. Geodetic Observations of Time-Variable Glacial Isostatic Adjustment in Southeast Alaska and Its Implications for Earth Rheology[J]. Journal of Geophysical Research: Solid Earth, 2019, 124(9): 9870-9889.

    [174]

    Zwally H J, Abdalati W, Herring T, et al. Surface Melt-Induced Acceleration of Greenland Ice-Sheet Flow[J]. Science, 2002, 297(5579): 218-222.

    [175]

    Das S B, Joughin I, Behn M D, et al. Fracture Propagation to the Base of the Greenland Ice Sheet During Supraglacial Lake Drainage[J]. Science, 2008, 320(5877): 778-781.

    [176]

    Pratt M J, Winberry J P, Wiens D A, et al. Seismic and Geodetic Evidence for Grounding-Line Control of Whillans Ice Stream Stick-Slip Events[J]. Journal of Geophysical Research: Earth Surface, 2014, 119(2): 333-348.

    [177] 艾松涛, 王泽民, 鄂栋臣, 等. 利用GPS的北极冰川运动监测与分析[J]. 武汉大学学报(信息科学版), 2012, 37(11): 1337-1340.

    Ai Songtao, Wang Zemin, Dongchen E, et al. Surface Movement Research of Arctic Glaciers Using GPS Method[J]. Geomatics and Information Science of Wuhan University, 2012, 37(11): 1337-1340.

    [178]

    Bartholomaus T C, Anderson R S, Anderson S P. Response of Glacier Basal Motion to Transient Water Storage[J]. Nature Geoscience, 2008, 1: 33-37.

    [179]

    Roeoesli C, Helmstetter A, Walter F, et al. Meltwater Influences on Deep Stick-Slip Icequakes near the Base of the Greenland Ice Sheet[J]. Journal of Geophysical Research: Earth Surface, 2016, 121(2): 223-240.

    [180]

    Liu Z, Dong D N, Lundgren P. Constraints on Time-Dependent Volcanic Source Models at Long Valley Caldera from 1996 to 2009 Using InSAR and Geodetic Measurements[J]. Geophysical Journal International, 2011, 187(3): 1283-1300.

    [181]

    Mattia M, Rossi M, Guglielmino F, et al. The Shallow Plumbing System of Stromboli Island as Imaged from 1 Hz Instantaneous GPS Positions[J]. Geophysical Research Letters, 2004, 31(24): L24610.

    [182]

    Sigmundsson F, Hreinsdóttir S, Hooper A, et al. Intrusion Triggering of the 2010 Eyjafjallajökull Explosive Eruption[J]. Nature, 2010, 468(7322): 426-430.

    [183] 顾国华, 王武星. GPS测得的2018年夏威夷6.9级地震与火山喷发地壳运动[J]. 武汉大学学报(信息科学版), 2019, 44(8): 1191-1197.

    Gu Guohua, Wang Wuxing. Crustal Motions Observed from GPS Observations for the M 6.9 Earthquake in Hawaii and the Eruption of the Kilauea Volcano in 2018[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1191-1197.

    [184] 张恒荣, 刘国明, 武成智, 等. 长白山天池火山监测与火山活动状态的初步分析[J]. 地震地质, 2003, 25(S1): 109-120.

    Zhang Hengrong, Liu Guoming, Wu Chengzhi, et al. Preliminary Study on the Active State of Changbaishan Tianchi Volcano[J]. Seismology and Geology, 2003, 25(S1): 109-120.

    [185] 陈国浒, 单新建, M.Moon Wooil, 等. 基于InSAR、GPS形变场的长白山地区火山岩浆囊参数模拟研究[J]. 地球物理学报, 2008, 51(4): 1085-1092.

    Chen Guohu, Shan Xinjian, Moon W M, et al. A Modeling of the Magma Chamber Beneath the Changbai Mountains Volcanic Area Constrained by InSAR and GPS Derived Deformation[J]. Chinese Journal of Geophysics, 2008, 51(4): 1085-1092.

    [186] 王凡, 沈正康, 王阎昭, 等. 2011年3月11日日本宫城Mw 9.0级地震对其周边地区火山活动的影响[J]. 科学通报, 2011, 56(14): 1080-1083.

    Wang Fan, Shen Zhengkang, Wang Yanzhao, et al. Influence of March 11th, 2011 Miyagi Mw 9.0 Earthquake on Volcanic Activities in Its Surrounding Areas[J]. Chinese Science Bulletin, 2011, 56(14): 1080-1083.

    [187]

    Biggs J, Pritchard M E. Global Volcano Monitoring: What Does It Mean when Volcanoes Deform?[J]. Elements, 2017, 13(1): 17-22.

    [188]

    Dzurisin D, Lisowski M, Wicks C W. Continuing Inflation at Three Sisters Volcanic Center, Central Oregon Cascade Range, USA, from GPS, Leveling, and InSAR Observations[J]. Bulletin of Volcanology, 2009, 71(10): 1091-1110.

    [189]

    Parks M M, Biggs J, England P, et al. Evolution of Santorini Volcano Dominated by Episodic and Rapid Fluxes of Melt from Depth[J]. Nature Geoscience, 2012, 5: 749-754.

    [190]

    Annen C, Blundy J D, Sparks R S J. The Genesis of Intermediate and Silicic Magmas in Deep Crustal Hot Zones[J]. Journal of Petrology, 2006, 47(3): 505-539.

    [191]

    Wiebe R A, Collins W J. Depositional Features and Stratigraphic Sections in Granitic Plutons: Implications for the Emplacement and Crystallization of Granitic Magma[J]. Journal of Structural Geology, 1998, 20(9/10): 1273-1289.

    [192]

    Head M, Hickey J, Gottsmann J, et al. The Influence of Viscoelastic Crustal Rheologies on Volcanic Ground Deformation: Insights from Models of Pressure and Volume Change[J]. Journal of Geophysical Research: Solid Earth, 2019, 124(8): 8127-8146.

    [193]

    Townsend M. Linking Surface Deformation to Thermal and Mechanical Magma Chamber Processes[J]. Earth and Planetary Science Letters, 2022, 577: 117272.

    [194] 黄润秋. 20世纪以来中国的大型滑坡及其发生机制[J]. 岩石力学与工程学报, 2007, 26(3): 433-454.

    Huang Runqiu. Large-Scale Landslides and Their Sliding Mechanisms in China Since the 20th Century[J]. Chinese Journal of Rock Mechanics and Engineering, 2007, 26(3): 433-454.

    [195] 张铎, 吴中海, 李家存, 等. 国内外地震滑坡研究综述[J]. 地质力学学报, 2013, 19(3): 225-241.

    Zhang Duo, Wu Zhonghai, Li Jiacun, et al. An Overview on Earthquake-Induced Landslide Research[J]. Journal of Geomechanics, 2013, 19(3): 225-241.

    [196]

    Yin Y P, Wang F W, Sun P. Landslide Hazards Triggered by the 2008 Wenchuan Earthquake, Sichuan, China[J]. Landslides, 2009, 6(2): 139-152.

    [197]

    Xu C, Xu X W, Yao X, et al. Three (Nearly) Complete Inventories of Landslides Triggered by the May 12, 2008 Wenchuan Mw 7.9 Earthquake of China and Their Spatial Distribution Statistical Analysis[J]. Landslides, 2014, 11(3): 441-461.

    [198]

    Delbridge B G, Bürgmann R, Fielding E, et al. Three-Dimensional Surface Deformation Derived from Airborne Interferometric UAVSAR: Application to the Slumgullion Landslide[J]. Journal of Geophysical Research: Solid Earth, 2016, 121(5): 3951-3977.

    [199]

    Malet J P, Maquaire O, Calais E. The Use of Global Positioning System Techniques for the Continuous Monitoring of Landslides: Application to the Super-Sauze Earthflow (Alpes-de-Haute-Provence, France)[J]. Geomorphology, 2002, 43(1/2): 33-54.

    [200]

    Hsu Y J, Chen R F, Lin C W, et al. Seasonal, Long-Term, and Short-Term Deformation in the Central Range of Taiwan Induced by Landslides[J]. Geology, 2014, 42(11): 991-994.

    [201]

    Wang G Q. GPS Landslide Monitoring: Single Base Vs. Network Solutions—A Case Study Based on the Puerto Rico and Virgin Islands Permanent GPS Network[J]. Journal of Geodetic Science, 2011, 1(3): 191-203.

    [202]

    Wu J H, Lin H M. Analyzing the Shear Strength Parameters of the Chiu-fen-erh-shan Landslide: Integrating Strong-Motion and GPS Data to Determine the Best-Fit Accelerogram[J]. GPS Solutions, 2009, 13(2): 153-163.

    [203]

    Axelrad P, Comp C J, Macdoran P F. SNR-Based Multipath Error Correction for GPS Differential Phase[J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(2): 650-660.

    [204] 金双根, 张勤耘, 钱晓东. 全球导航卫星系统反射测量(GNSS+R)最新进展与应用前景[J]. 测绘学报, 2017, 46(10): 1389-1398.

    Jin Shuanggen, Zhang Qinyun, Qian Xiaodong. New Progress and Application Prospects of Global Navigation Satellite System Reflectometry (GNSS+R)[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1389-1398.

    [205]

    Larson K M. GPS Interferometric Reflectometry: Applications to Surface Soil Moisture, Snow Depth, and Vegetation Water Content in the Western United States[J]. WIREs Water, 2016, 3(6): 775-787.

    [206]

    Jacobson M D. Dielectric-Covered Ground Reflectors in GPS Multipath Reception—Theory and Measurement[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(3): 396-399.

    [207]

    Larson K M, Gutmann E D, Zavorotny V U, et al. Can We Measure Snow Depth with GPS Receivers?[J]. Geophysical Research Letters, 2009, 36(17): L17502.

    [208]

    Nievinski F G, Larson K M. Inverse Modeling of GPS Multipath for Snow Depth Estimation: Part I: Formulation and Simulations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6555-6563.

    [209]

    Nievinski F G, Larson K M. Inverse Modeling of GPS Multipath for Snow Depth Estimation: Part II: Application and Validation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6564-6573.

    [210]

    McCreight J L, Small E E, Larson K M. Snow Depth, Density, and SWE Estimates Derived from GPS Reflection Data: Validation in the Western U. S[J]. Water Resources Research, 2014, 50(8): 6892-6909.

    [211] 王佳彤, 胡羽丰, 李振洪, 等. 利用GPS-IR技术快速估计雪水当量[J]. 武汉大学学报(信息科学版), 2021, 46(11): 1666-1676.

    Wang Jiatong, Hu Yufeng, Li Zhenhong, et al. Rapid Estimation of Snow Water Equivalent Using GPS-IR Observations[J]. Geomatics and Information Science of Wuhan University, 2021, 46(11): 1666-1676.

    [212]

    Zhang Z Y, Guo F, Zhang X H. Triple-Frequency Multi-GNSS Reflectometry Snow Depth Retrieval by Using Clustering and Normalization Algorithm to Compensate Terrain Variation[J]. GPS Solutions, 2020, 24(2): 52.

    [213]

    Wang J W, Yuan Q Q, Shen H F, et al. Estimating Snow Depth by Combining Satellite Data and Ground-Based Observations over Alaska: A Deep Learning Approach[J]. Journal of Hydrology, 2020, 585: 124828.

    [214]

    Larson K M, Small E E, Gutmann E, et al. Using GPS Multipath to Measure Soil Moisture Fluctuations: Initial Results[J]. GPS Solutions, 2008, 12(3): 173-177.

    [215]

    Larson K M, Small E E, Gutmann E D, et al. Use of GPS Receivers as a Soil Moisture Network for Water Cycle Studies[J]. Geophysical Research Letters, 2008, 35(24): L24405.

    [216]

    Chew C C, Small E E, Larson K M, et al. Effects of Near-Surface Soil Moisture on GPS SNR Data: Development of a Retrieval Algorithm for Soil Moisture[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 537-543.

    [217]

    Small E E, Larson K M, Chew C C, et al. Validation of GPS-IR Soil Moisture Retrievals: Comparison of Different Algorithms to Remove Vegetation Effects[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(10): 4759-4770.

    [218]

    Edokossi K, Calabia A, Jin S G, et al. GNSS-Reflectometry and Remote Sensing of Soil Moisture: A Review of Measurement Techniques, Methods, and Applications[J]. Remote Sensing, 2020, 12(4): 614.

    [219]

    Small E E, Larson K M, Braun J J. Sensing Vegetation Growth with Reflected GPS Signals[J]. Geophysical Research Letters, 2010, 37(12): L12401.

    [220]

    Wan W, Larson K M, Small E E, et al. Using Geodetic GPS Receivers to Measure Vegetation Water Content[J]. GPS Solutions, 2015, 19(2): 237-248.

    [221]

    Larson K M, Small E E. Normalized Microwave Reflection Index: A Vegetation Measurement Derived from GPS Networks[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(5): 1501-1511.

    [222]

    Small E E, Larson K M, Smith W K. Normalized Microwave Reflection Index: Validation of Vegetation Water Content Estimates from Montana Grasslands[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(5): 1512-1521.

    [223]

    Evans S G, Small E E, Larson K M. Comparison of Vegetation Phenology in the Western USA Determined from Reflected GPS Microwave Signals and NDVI[J]. International Journal of Remote Sensing, 2014, 35(9): 2996-3017.

    [224]

    Zhang S C, Wang T, Wang L X, et al. Evaluation of GNSS-IR for Retrieving Soil Moisture and Vegetation Growth Characteristics in Wheat Farmland[J]. Journal of Surveying Engineering, 2021, 147(3): 04021009.

    [225]

    Wöppelmann G, Marcos M. Vertical Land Motion as a Key to Understanding Sea Level Change and Variability[J]. Reviews of Geophysics, 2016, 54(1): 64-92.

    [226]

    Larson K M, Löfgren J S, Haas R. Coastal Sea Level Measurements Using a Single Geodetic GPS Receiver[J]. Advances in Space Research, 2013, 51(8): 1301-1310.

    [227]

    Larson K M, Ray R D, Nievinski F G, et al. The Accidental Tide Gauge: A GPS Reflection Case Study from Kachemak Bay, Alaska[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1200-1204.

    [228]

    Larson K M, Ray R D, Williams S D P. A 10-Year Comparison of Water Levels Measured with a Geodetic GPS Receiver Versus a Conventional Tide Gauge[J]. Journal of Atmospheric and Oceanic Technology, 2017, 34(2): 295-307.

    [229]

    Löfgren J S, Haas R. Sea Level Measurements Using Multi-frequency GPS and GLONASS Observations[J]. EURASIP Journal on Advances in Signal Processing, 2014, 2014(1): 50.

    [230]

    Wang X L, Zhang Q, Zhang S C. Water Levels Measured with SNR Using Wavelet Decomposition and Lomb-Scargle Periodogram[J]. GPS Solutions, 2017, 22(1): 22.

    [231]

    Wang X L, Zhang Q, Zhang S C. Sea Level Estimation from SNR Data of Geodetic Receivers Using Wavelet Analysis[J]. GPS Solutions, 2018, 23(1): 6.

    [232]

    Peng D J, Feng L J, Larson K M, et al. Measuring Coastal Absolute Sea-Level Changes Using GNSS Interferometric Reflectometry[J]. Remote Sensing, 2021, 13(21): 4319.

    [233]

    Peng D J, Hill E M, Li L L, et al. Application of GNSS Interferometric Reflectometry for Detecting Storm Surges[J]. GPS Solutions, 2019, 23(2): 47.

    [234]

    Larson K M, Lay T, Yamazaki Y, et al. Dynamic Sea Level Variation from GNSS: 2020 Shumagin Earthquake Tsunami Resonance and Hurricane Laura[J]. Geophysical Research Letters, 2021, 48(4): e2020GL091378.

    [235]

    Wang X L, He X F, Shi J, et al. Estimating Sea Level, Wind Direction, Significant Wave Height, and Wave Peak Period Using a Geodetic GNSS Receiver[J]. Remote Sensing of Environment, 2022, 279: 113135.

    [236]

    Rogers G, Dragert H. Episodic Tremor and Slip on the Cascadia Subduction Zone: The Chatter of Silent Slip[J]. Science, 2003, 300(5627): 1942-1943.

    [237] 单新建, 尹昊, 刘晓东, 等. 高频GNSS实时地震学与地震预警研究现状[J]. 地球物理学报, 2019, 62(8): 3043-3052.

    Shan Xinjian, Yin Hao, Liu Xiaodong, et al. High-Rate Real-Time GNSS Seismology and Early Warning of Earthquakes[J]. Chinese Journal of Geophysics, 2019, 62(8): 3043-3052.

    [238]

    Fu Y N, Freymueller J T. Seasonal and Long-Term Vertical Deformation in the Nepal Himalaya Constrained by GPS and GRACE Measurements[J]. Journal of Geophysical Research: Solid Earth, 2012, 117(B3): B03407.

    [239]

    Hao M, Wang Q L, Shen Z K, et al. Present Day Crustal Vertical Movement Inferred from Precise Leveling Data in Eastern Margin of Tibetan Plateau[J]. Tectonophysics, 2014, 632: 281-292.

    [240]

    Riddell A R, King M A, Watson C S. Present-Day Vertical Land Motion of Australia from GPS Observations and Geophysical Models[J]. Journal of Geophysical Research: Solid Earth, 2020, 125(2): e2019JB018034.

  • 期刊类型引用(1)

    1. 《中国公路学报》编辑部. 中国桥梁工程学术研究综述·2024. 中国公路学报. 2024(12): 1-160 . 百度学术

    其他类型引用(0)

图(1)
计量
  • 文章访问数:  978
  • HTML全文浏览量:  161
  • PDF下载量:  219
  • 被引次数: 1
出版历程
  • 收稿日期:  2023-01-15
  • 网络出版日期:  2023-01-15
  • 刊出日期:  2024-12-04

目录

/

返回文章
返回