GAMIT/GLOBK 10.4 GPS分析教程

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"gamit10.4的说明文档和教程，详细的解释了gamit/globk的基本步骤" 本文档是关于GAMIT/GLOBK 10.4版本的详细说明，它是一个全面的全球定位系统（GPS）分析软件包，由麻省理工学院、哈佛-史密森天体物理中心和斯克里普斯海洋学研究所共同开发。该软件主要用于估算地面站的坐标和速度，以及处理与之相关的随机或确定性模型。 1. GPS测量与分析 - 相位和伪距观测：GAMIT/GLOBK可以处理两种基本的GPS观测数据类型，相位观测提供了高精度的位置信息，而伪距观测则适用于更广泛的环境，但精度较低。 - 卫星和站点运动建模：软件考虑了地球自转、大气延迟、卫星轨道等对GPS信号的影响，以准确地计算地面站的位置和速度。 - 参数估计：通过对观测数据进行分析，软件能够估计出各种参数，包括站坐标、钟差、电离层延迟等，同时考虑了各种误差源。 2. 自动处理流程 - 设置：用户需要配置一系列控制文件来定义处理参数，如数据选择、解算策略等。 - 程序流程：GAMIT/GLOBK有一套自动化的处理流程，包括数据预处理、解算、后处理等阶段。 - 控制文件编辑：用户需根据实际需求调整控制文件内容，以适应不同的分析任务。 - sh_gamit和sh_glred：这两个脚本分别用于启动GAMIT和GLOBK的批处理，进行数据处理和结果归一化。 3. 结果评估 - 结果评估是检查分析质量的重要环节，包括对残差、统计特性等的分析，以确保结果的可靠性和准确性。 4. 时间序列与速度场生成 - h文件组合：通过合并多个处理周期的h文件，可以获得长期的时间序列数据。 - 数据编辑：在生成时间序列之前，可能需要对原始数据进行清洗和筛选，去除异常值或错误记录。 - 数据权重：根据数据的不确定性设置合适的权重，以反映不同观测的可靠性。 - 实现参考框架：GAMIT/GLOBK可以建立一个固定的或漂移的参考框架，用于描述地面站的位置变化。 - 生成时间序列和速度解决方案：软件能输出地面站随时间的坐标变化和速度矢量。 5. 参考文献提供了进一步阅读和研究的相关资料，包括GAMIT/GLOBK的技术细节和已发表的科研论文。 GAMIT/GLOBK提供了一套强大的工具，用于科学研究和地壳形变监测，其详细的教程和自动化流程使得用户能够高效地处理和分析GPS数据。通过这个软件，用户可以获取精确的地表运动信息，支持地震学、地质学、地球动力学等多个领域的研究。

26 October 2010

The uncertainties in a GPS analysis, however, cannot be treated with white noise

statistics because errors with temporal correlations dominate both the phase observations

and estimates of station coordinates (the quasi-observations input to GLOBK). In the

phase residuals (from cview or the sky plots produced by sh_gamit), the visible noise—

from multipathing and tropospheric fluctuations—is typically correlated over spans of

15-30 minutes. This implies that only samples taken at these intervals are independent,

and, to a first approximation, we would get realistic uncertainties by multiplying the

formal errors by the square root of the ratio of this interval to the sampling interval

used—e.g., for the 2 minute sampling commonly used in solve, we would increase the

uncertainties by a factor of 3-5. There also errors with longer correlation times that do

not show up in the residuals but are absorbed into the parameter adjustments. Assessing

the magnitude of these errors requires us to use noise visible in the residuals (phase or

coordinates) to infer the character of the noise at lower frequencies. There are a number

of excellent studies of the character of GPS errors discussed in the presentation

‘Error_Analysis.ppt’ from our most recent workshops on the Documentation page of the

GAMIT/GLOBK web site (e.g., Mao et al. [1999], Dixon et al. [2000], and Williams

[2003]). Once you have adopted a particular weighting of the data, it is often possible to

use external knowledge of the expected behavior of the coordinates or velocities to

validate the uncertainties; see McClusky et al. [2000], Davis et al. [2003], and McCaffrey

et al. [2006].

In GAMIT/GLOBK there are several ways you can control the uncertainties you obtain

for coordinates and velocities, and it is important for you to keep clearly in mind how

each of these operates. The uncertainties generated by solve and passed to GLOBK in the

h-file are determined by the a priori error assigned to the phase observations and by the

sampling interval—solve does not rescale by the square root of χ

/df (“postfit nrms” in

the q-file). In the initial (“preliminary”) solution, we normally assign an uncertainty of

10 mm to each one-way L1 phase. By Equation (1), the assigned uncertainty in an LC

phase becomes 32 mm. The mean rms of one-way LC residuals is typically ~6-9 mm, so

the nrms computed by solve is 0.2-0.3. In the second (“final”) solution, we normally

reweight the observations using a constant and elevation-dependent term computed in

data editing by program autcln from the actual (one-way LC) phase residuals. In order to

keep the overall weighting approximately the same as with the 10 mm constant error, the

values computed by autcln (ATLEV table in file autcln.post.sum) are multiplied by an

arbitrary factor of 1.7 (in script sh_sigelv) before being input to solve (via the N-file). We

chose to use inflated values of the a priori phase error and not rescale by the nrms in

order to generate coordinate uncertainties that (in the presence of correlated noise) are

approximately realistic with 2-minute sampling. An equally valid approach would be to

rescale by the nrms (i.e. make χ

/df =1.0) and compensate later for the unrealistically low

coordinate uncertainties. With whatever weighting you use in solve, you can increase the

coordinate uncertainties used by GLOBK by rescaling all covariances on the h-file or by

adding white noise or random-walk noise to the variances of individual stations. We

discuss in Section 4.3 why the latter approach is usually preferred.

26 October 2010

To obtain meaningful estimates of crustal motion, it is necessary to define a reference

frame by imposing constraints on the solution. These come in two common flavors:

With finite constraints, we assign realistic a priori uncertainties to the coordinates and

velocities of one or more sites. This is the only option available in GAMIT and it is also

available in GLOBK. A minimum (non-redundant) constraint with this approach would

involve restricting translation by fixing or tightly constraining the three coordinates of

one site, and restricting rotation by constraining Earth orientation. Constraining

additional sites provides redundancy, but can distort the network and remove your ability

to detect errors in those sites except through an increase in χ

. The second type of

constraint, available through the glorg program of GLOBK, is generalized. With this

approach, we choose as large a set as possible of sites with good a priori values and

minimize their adjustments while estimating an overall translation, rotation, and scale

(Helmut parameters) of the network. Since all of the frame-defining sites are free to

move, outliers can be readily detected and removed. Moreover, with generalized

constraints there can be no internal distortion of the network: all realizations of the

reference frame will differ only by a translation and rotation. See Dong et al., [1998] for

a mathematical description of each of these approaches.

2. Automatic Processing with GAMIT and GLOBK

GAMIT is composed of distinct programs which perform the functions of preparing the

data for processing (makexp and makex), generating reference orbit and rotation values

for the satellites (arc, yawtab), interpolating time- and location-specific values of

atmospheric and loading models (grdtab), computing residual observations (O-C's) and

partial derivatives from a geometrical model (model), detecting outliers or breaks in the

data (autcln), and performing a least squares analysis (solve). Although the modules can

be run individually, they are tied together through the data flow, particularly file-naming

conventions, in such a way that most processing is best done with shell scripts and a

sequence of batch files set up by a driver module (fixdrv) for modeling, editing, and

estimation. Though the data editing is almost always performed automatically, the

solution residuals can be displayed or plotted so that problematic data can be identified

(cview).

Likewise, GLOBK operates through distinct programs, which can be invoked with a

single command or run separately. The primary functions are to combine quasi-

observations--either GAMIT/GLOBK “h-files” or the internationally accepted SINEX

format--from multiple networks and/or epochs (glred or globk), and to impose on this

solution a reference frame appropriate to the scientific objective (glorg). Note that globk

and glred are the same program, just called in different modes: glred to read data from

one day at a time for generating time series, globk for stacking multiple epochs to obtain

a mean position and/or velocity.

The full sequence of steps to take you from phase data to time series is accomplished

with two shell scripts: sh_gamit looks for raw or RINEX data over a range of days and

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Andjm

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