Short-session Static and Kinematic Processing

1. Short-session Static and Kinematic Processing Short-session static: GAMIT processing, sessions 1-3 hours long Kinematic: TRACK processing, coordinates estimated at each epoch; one or more sites may be moving • High frequency multipath and sometimes atmospheric errors dominate and fail to average • Shorter satellite tracks mean less time to separate the signatures in the data, leading to higher correlations and thus higher uncertainties in coordinates and ambiguity parameters • Less averaging of noise and higher geometric uncertainties make ambiguity resolution more difficult; when it fails with short tracks, the uncertainties become much larger

2. Results may vary significantly with time of day A 2-hr session between 12:00 and 16:00 (left plot) would be much more likely to resolve ambiguities than a 2-hr session between 20:00 an 24:00 (right plot)

3. Continuous network in Italy used to test the effect of session length and network configurationon coordinate repeatabilities Test site is PRATFiruzabadi and King (2011)

4. Time series for 2-hr sessions with 4 reference sites.Note large sigmas on day 61 and outliers on days 62 and 63; with these removed the rms is 2 mm horizontal and 7 mm vertical

5. Precision vs Session Length for Network Processing Bars show repeatability in position over 31 days of test site with respect to networks of 2 to 16 sites spanning 180-600 km. With at least 4 reference stations, outliers were less than 5% for sessions of 2 hrs or more.

6. Precision vs Session Length for Single Baselines Bars show repeatability in position over 31 days of a test site with respect to each of 7 sites 26-585 km away in single-baseline processing. 10% of the 1- and 2-hr sessions had large uncertainties and were omitted. 1-hr results degrade significantly for baselines longer than 100 km

7. Cerca del Cielo earthflow, Ponce, Puerto Rico 10 GPS monuments (including one continuous) on the landslide, and 2 reference monuments outside Steady-state flow 0.5 - 2.0 mm/d Maximum ~ 100 mm/d From G. Wang, 2010

8. Time series of bi-weekly GPS surveys Mar-Dec 2008 20-minute occupations GPS 07 and 13 near the head scarp GK03 and GP18 mid-slope From G. Wang, 2010

9. Time series of hourly and daily positions over 10 days

10. GAMIT Settings for Sessions < 3 Hours • Consider using 15s sampling • Run sh_gamit with the –sesinfo option specifying the start time, sampling interval, and number of epochs • If more than one session per day, run sh_gamit with the –netext option, using a different letter for each session • Don’t decimate the preliminary or final solutions Decimation factor = 1 Quick-pre decimation factor = 1

11. Kinematic GPS • The style of GPS data collection and processing suggests that one or more GPS stations is moving (e.g., car, aircraft). • The moving stations are kept stationary at the beginning and/or end of the track to resolve ambiguities; then phase lock is maintained (as best as possible) through the track • To obtain good results for positioning as a function of time it helps if the ambiguities can be fixed to integer values. Although with the “back smooth” option in track this is not so critical. • Program ‘track’ is the MIT implementation of this type of processing • Unlike many kinematic processors,track pre-reads all data before processing. (But there is a real-time version, trackRT.)

12. General aspects • The success of kinematic processing depends on separation of sites • There are one or more static base stations and the moving receivers are positioned relative to these. • For separations < 10 km, usually easy • 10>100 km more difficult but often successful • >100 km very mixed results depending on quality of data collected. (Seismological example results are from 400km baselines.)

13. Track features • Track uses the Melbourne-Wubbena Wide Lane to resolve L1-L2 and then a combination of techniques to determine L1 and L2 cycles separately. • “Bias flags” are added at times of cycle slips and the ambiguity resolution tries to resolve these to integer values. • Track uses floating point estimate with LC, MW-WL and ionospheric delay constraints to determine the integer biases and the reliability with which they are determined. • Kalman filter smoothing can be used. (Non-resolved ambiguity parameters are constant, and atmospheric delays are consistent with process noise). When atmospheric delays are estimated, the smoothing option should always be used.

14. Basic input • Track runs using a command file • The base inputs needed are: • Obs_file specifies names of rinex data files. Sites can be K kinematic or F fixed • Nav_file orbit file either broadcast ephemeris file or sp3 file • Mode air/short/long -- Mode command is not strictly needed but it sets defaults for variety of situations

15. Some results • Moving vehicle used for gravity measurments: 5-second sampling with stop and go • GPS seismology: 1 HZ tracking of earthquake surface wave arrivals

16. Track of Map-view track of vehicle motion over 8 km

17. Vehicle height vs time

18. Zoom of height just before power failure

19. Surface waves from the December, 2000, M 6 San Simeon, Calliforna earthquake1 Hz sampling

20. Detail around arrival time. Descriptiion and data on web site.

21. Summary • Under favorable conditions and especially for short inter-site distances, both short-session static and kinematic processing can produce excellent results • Use of more than a single reference site improves reliability • TRACK’s forward-backward filtering improves reliablity of non-real-time kinematic tracking -- BUT there is now a real-time version (trackRT) available