Error Modeling Thomas Herring Room ;

06/13/06Unavco GG Lec 032 Overview Notations Error modeling of GPS phase measurements Error modeling in time series analyses

06/13/06Unavco GG Lec 033 Notations Terms that are used in gamit/globk Weighted-root-mean-square (WRMS) scatter: square root of the mean of residuals squared weighted by the estimated variance of the residuals. Normalized-root-mean-square (NRMS) scatter: square root of mean of sum of residuals divided by their standard deviations squared. Square-root of chi- squared per degree of freedom. Should be near unity.

06/13/06Unavco GG Lec 034 GPS phase noise modeling A common assumption in GPS analyses is to assume that all phase data has the same random noise level. Examination of phase residuals (phs_res_root option in autcln.cmd) shows this is not the case. Default gamit processing uses a site dependent elevation angle dependent model The model is computed in autcln and passed to solve through the n-file (noise file). The noise levels are increased so that gamit NRMS of phase residuals is Short-term scatter of position estimates suggest sigma 1-2 times too small. (Positions estimates do not depend on sampling rate to about 4 minutes).

06/13/06Unavco GG Lec 035 Globk re-weighting There are methods in globk to change the standard deviations of the position (and other parameter) estimates. Complete solutions: –In the gdl files, variance rescaling factor and diagonal rescaling factors can be added. –First factor scales the whole covariance matrix. Useful when Using SINEX files from different programs Accounting for different sampling rates –Second factor is not normally needed and is used to solve numerical instability problems. Scales diagonal of covariance matrix. Needed in some SINEX files Large globk combinations (negative chi-square increments) Large combinations are best done with pre-combinations in to weekly or monthly solutions Individual sites with sig_neu command. Wild cards allowded in site names (both beginning and end)

06/13/06Unavco GG Lec 036 Time series noise The most complicated aspect of GPS timeseries analysis: Basic problem: Errors in GPS position estimates are both spatially and temporally correlated Spatial correlations: –Handled in glorg by using a local definition of reference frame. Stable sites in the area are used in the determining translation and rotation of local frame. Generally, the smaller the area the smaller the RMS scatter of position estimates. (Median WRMS scatter for sites in Southern California <1 mm horizontal and <3 mm vertical; Same sites for North America frame ~1 mm and 3 mm. Temporal correlations: –More difficult because stochastic process of noise(s) not known and for velocity sigma estimates lowest (least well determined part of spectrum needed)

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06/13/06Unavco GG Lec 039 Spectrum for Non-stable GPS site California frame: Non-Stable site

06/13/06Unavco GG Lec 0310 PSD: North America Frame

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06/13/06Unavco GG Lec 0315 Example Power Spectral Density Southern California reference frame Very stable sites

06/13/06Unavco GG Lec 0316 PSD of same site in North America frame

06/13/06Unavco GG Lec 0317 “Realistic Sigma” Algorithm Motivation was to develop an algorithm that could quickly estimate standard deviations of parameters estimated from time series that could have gaps and variable quality data Concept: Common practice is to scale standard deviations of parameter estimates by the scatter of the post-fit residuals: Valid if residuals are white noise. Scatter of residuals is characterized by chi-squared per degree of freedom: should near unity. If data are averaged over some interval (e.g., 1 week or month), then fit of parameters (such as rate) rarely affected but the chi- squared of averaged residuals will increase if residuals are not white noise: Hence scale by new chi-squared: But if longer averages are used, chi-squared keeps increasing so scale standard deviations by chi-squared obtained with infinite time averaging. Later step requires as method for extrapolating chi- squared estimates from short averaging times to long ones. We use a first-order Gauss Markov model to this.

06/13/06Unavco GG Lec 0318 Algorithm Time series parameters are estimated using standard weighted least squares Postfit residuals from this fit are averaged from successive longer intervals and chi-squared computed for the averaged residuals. A first-order Gauss Markov process model (characterized by a variance and correlation time) is fit to the estimates of chi-squared. The chi-squared value for infinite averaging time predicted from this model is used to scale the white- noise sigma estimates from the original fit. Example: CVHS (part of the Baldwin Park event)

06/13/06Unavco GG Lec 0319 Raw times series (error bars not shown)

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06/13/06Unavco GG Lec 0321 Red Lines are realistic sigma of rate estimates

06/13/06Unavco GG Lec 0322 Realistic Sigma chi-squares for East component

06/13/06Unavco GG Lec 0323 Site with post-2005 data. Notice realistic error estimate

06/13/06Unavco GG Lec 0324 Chi-square with averaging for pre and post-2005 data

06/13/06Unavco GG Lec 0325 How well to estimate agree? Rate comparisons: Comp 2005 mm/yr  mm/yr North ± ± East ± ± Height ± ± Adding the additional data after 2005, make rate sigma increase factor 5. In this case, algorithm seems to do a reasonable job of computing sigma.

06/13/06Unavco GG Lec 0326 Summary All algorithms for computing estimates of standard deviations have various problems: Fundamentally, rate standard deviations are dependent on low frequency part of noise spectrum which is poorly determined. Assumptions of stationarity are often not valid (example shown) “Realistic sigma” algorithm implemented in tsview and enfit/ensum; sh_gen_stats generates mar_neu commands for globk based on the noise estimates