GPS Surveying & Data Processing in Menlo Park

1. GPS Surveying & Data Processing in Menlo Park J.L. Svarc and J.R. Murray-Moraleda, U.S. Geological Survey, Menlo Park, CA

2. Coverage & Surveys of Campaign Nets

3. USGS Campaign GPS 1992-2008

5. USGS Campaign GPS in the San Francisco Bay Area

6. Campaign and PBO stations near Lake Pillsbury

7. Typical campaign GPS network velocities. • 4-5 year time span gives velocity uncertainties of ~ 1 mm/yr

8. Typical campaign GPS time series • Links in left nav bar lead to other post processed results

9. All stations have station information pages with links to field logs, photos, maps, and descriptions

10. Gipsy processing of Campaign & Continuous Data

11. All data processed using precise point positioning strategy. Orbits and clock files supplied by JPL

12. All data processed using precise point positioning strategy. Orbits and clock files supplied by JPL • Continuous networks are processed using ambizap to do bias fixing. This allows networks of several hundred stations to be processed together

13. All data processed using precise point positioning strategy. Orbits and clock files supplied by JPL • Continuous networks are processed using ambizap to do bias fixing. This allows networks of several hundred stations to be processed together • Continuous nets results are cleaned and detrended. Offsets and seasonal signals are removed to obtain the best velocity estimates

14. All data processed using precise point positioning strategy. Orbits and clock files supplied by JPL • Continuous networks are processed using ambizap to do bias fixing. This allows networks of several hundred stations to be processed together • Continuous nets results are cleaned and detrended. Offsets and seasonal signals are removed to obtain the best velocity estimates • Results are updated daily on web pages

15. Approximately 1000 Continuous GPS stations processed daily • Processed as 7 regional networks

16. Pacific Northwest

17. Pacific Northwest • Northern California

18. Pacific Northwest • Northern California • San Francisco Bay Area

19. Pacific Northwest • Northern California • San Francisco Bay Area • Central California

20. Pacific Northwest • Northern California • San Francisco Bay Area • Central California • Central Nevada

21. Pacific Northwest • Northern California • San Francisco Bay Area • Central California • Central Nevada • Long Valley

22. Pacific Northwest • Northern California • San Francisco Bay Area • Central California • Central Nevada • Long Valley • Yellowstone

23. All station velocities are with respect to fixed North America. • Color coding of stations allows for quick evaluation of station health. • Stations are clickable and link to time series • Epicenters on maps are updated daily. • Data automatically processed on 2 linux boxes. No more than two hours a day spent on processing unless problems are encountered

24. Regional Filtering • Instead of using a global set of stations for reference frame transformation, use a regional set of local stations • Removes a significant amount of common mode noise • Will not work with data older than ~1999

25. Station position with respect to fixed North America

26. Station position with respect to fixed North America using a regional set of stations to remove common mode error • Scatter in time series is cut in half

27. Detrended regionally filtered results showing calculated offsets and seasonal trends

28. Monitoring Methods, Usage & Availability of Processed Data

29. All detrended time series displayed by latitude or longitude since 2005 or the last 90 days

30. Station velocities relative to fixed NA • ITRF2000 Station velocities • Station velocities with offsets and seasonal signals removed Rn = 3.0 Re = 4.0 Ru = 3.0 R is a scaling factor applied to GIPSY formal errors Latitude Longitude Vn Ve Sn Se Corr Sta Vu Su 39.109302 -122.303869 6.35 -10.45 0.69 0.70 -0.0279 208P 1.81 0.89 37.749435 -121.977955 15.24 -14.42 0.59 0.59 -0.0160 229P 2.05 0.70 37.919405 -122.152552 18.82 -13.26 0.34 0.34 0.0048 BRIB 64.52 0.35 37.724115 -122.119309 19.82 -16.04 0.33 0.33 0.0035 CHAB -0.09 0.33 39.432639 -121.664962 7.82 -9.92 0.34 0.35 0.0114 CHO1 -0.48 0.35 38.034176 -120.386040 8.91 -10.09 0.35 0.35 -0.0916 CMBB -0.13 0.36 37.896405 -121.278495 8.85 -12.37 0.38 0.38 0.0262 CNDR -3.38 0.40 37.878575 -121.915627 12.20 -11.81 0.33 0.33 -0.0306 DIAB 0.11 0.34 . .

31. All campaign data are archived in Menlo and sent to Berkeley as a backup • All stacov files are archived in Menlo and available on request • All field logs, photos, and Google Earth files are available on line

32. New Directions in Monitoring of Continuous Data • Use John’s noise estimation code to routinely look for new offsets in time series and changes in rate

33. New Directions in Monitoring of Continuous Data • Use John’s noise estimation code to routinely look for new offsets in time series and changes in rate • Monitor baseline length changes in networks to determine spatial changes

34. New Directions in Monitoring of Continuous Data • Use John’s noise estimation code to routinely look for new offsets in time series and changes in rate • Monitor baseline length changes in networks to determine spatial changes • Install latest version of GIPSY (released 6-3-2008)

35. Opportunities • Leverage PBO data to increase our monitoring capabilities and to focus our field efforts • Obtain external funding through new targeted investigations • Take advantage of advancements in processing and reference frame strategies Challenges • What campaign networks do we continue to survey? Which new campaign networks should be started? • Should we process PBO data in-house? Which sites? • How much of our work should be dictated by reimbursable funding efforts?

36. Recommended Actions • Continue to process PBO data along with our campaign data to produce self-consistent data products (more on this from Nancy) • Continue implementation of new processing strategies • Maintain campaign GPS activities for ongoing projects and initiate targeted data collection where necessary to answer specific questions • Increase focus on monitoring of continuous data (and the use of high-rate data, to be covered later) • Evaluate time and resources available for future reimbursable funding opportunities