1. Are thermal effects responsible for micron-level motions recorded at deep- and shallow-braced monuments in a short-baseline network at Yucca Mountain, Nevada? Emma Hill, Jim Davis, Pedro Elosegui, Brian Wernicke, Eric Malikowski, and Nathan Niemi Station REPO

2. Introduction Baseline lengths SLI4-SLID (Slide Mtn): 0 m REPO-REP2: ~10 m REPO-REP3: ~100 m REPO-REP4: ~1000 m (Similar instrumentation) Blue dots = BARGEN sites Southern Nevada Desert environment ‘Low’ tectonic rates

3. Data Processing Data processed using GAMIT: Fixed orbits (IGS final) No TZD estimation L1-only position estimates Only look at baselines Site-specific effects: Phase errors Un-modeled physical motions Photo by Beth Bartel

4. Time Series - East Short baseline Annual cycles: 0.03-0.54 mm RMS: 0.06-0.20 mm Time series have been offset for illustration RMS calculated about model of seasonal cycle Zero baseline (ZBL) RMS: 0.03 mm

5. Time Series - North Short baseline Annual cycles: 0.02-0.19 mm RMS: 0.06-0.23 mm Zero baseline (ZBL) RMS: 0.03 mm

6. Time Series - Radial Short baseline Annual cycles: 0.10-0.40 mm RMS: 0.12-0.73 mm Zero baseline (ZBL) RMS: 0.08 mm

7. Temperature Data Temperature data was obtained from the Beatty weather station (~25 km NW of Yucca Mountain)

8. Longer-period Signals “Longer-period” signals (quasi-periodic) = Gaussian-filtered time series REP2-REPO (~10 m baseline) For illustration, the north component time series has been reversed (i.e. figure shows REPO-REP2 for the north)

9. Longer-period Signals REP2-REPO REP4-REPO Cross-correlation between temperature and GPS time series The GPS seasonal cycles might lag those of the temperature data, but it is hard to detect using this method. Correlation coefficients: East = 0.74-0.98 North = 0.45-0.93 Radial = 0.55-0.76 Correlation coefficient

10. Monte Carlo Analysis 1.Add noise to GPS and temperature time series 2.Gaussian filter to get long- and short-period signals 3.Cross-correlation as before 4.Record peak correlation and corresponding time step 5.Repeat 5000 times

11. Longer-period Signals Monte Carlo analysis (cross-correlation between temperature and GPS) Indicates a lag (15-30 days) for many baselines in the horizontal component… EAST: Similar results for all other baselines to REP4 (no lag for shorter baselines). NORTH: Similar results for all other baselines to REPO (no correlation for other baselines).

12. Longer-period Signals … but we do not see a lag for the radial component. The temperature ‘lags’ the GPS by >50 days. Although there is a correlation for the radial, it looks like we are comparing two periodic signals that do not appear to be related.

13. Shorter-period Signals “Shorter-period” signals = residuals from Gaussian-filtered time series REP2-REPO (~10 m baseline)

14. Shorter-period Signals REP2-REPO (~10m) REP3-REPO (~90 m) Both regular cross-correlation… … and Monte Carlo technique indicate no lag time for short-period signals Highest correlation (0.67) for the east component and baselines to REP2 (shallow-braced monument) Correlation coefficient

15. Thermal Expansion Monument (shorter-period?) - Different leg lengths and orientation - REP2 different type of pipe Cliff / Bedrock (longer-period?) - Dong et al. [2002] estimate ~45 day lag - Differential effects from orientation of ridgeline? Upper ground layers (shorter-period?) - Deep versus shallow-braced monuments Red = longest leg Green = shortest leg Several processes occurring at different time-scales? Steep cliff Gradual slope Something else?

16. Baseline-dependent Noise ~0.2 mm/km ~0.3 mm/km ~0.8 mm/km Orbits? ~0.002 mm over 1 km (assuming 5 cm accuracy of IGS final orbits) Troposphere? Ionosphere? Multipath? (and what is causing the seasonal cycles in the radial?)

17. Tropospheric Delay REP4-REPO When TZD parameters are estimated: Time series for horizontal components are very similar. But seasonal cycles in the radial component are reduced by ~50% for the longer baselines. No TZD estimation With TZD estimation A mean has been removed from both time series

18. Ionospheric Delay When LC is used: Time series are considerably noisier and have visible receiver change offsets. Seasonal signals remain. REP4-REPO L1-only LC A mean has been removed from both time series Differences between L1- and L2-only (Receiver changes at REP4, Jan and Nov 2007 (NetRS to 4000 SSI to NetRS) REP4-REPO

19. Elevation-Angle Dependence Multipath? Antenna differences? Time series from results using different elevation cutoff angles are offset. Largest effect in radial component. REP3-REP2 (~10m) REP4-REP2 (~900 m) Y-axes have different scales

20. Conclusions The sites appear to be very stable (RMS 0.06-0.73 mm). However, the time series do show both seasonal (annual amplitude 0.03-0.54 mm) and shorter-period signals. We suspect the horizontal seasonal signals may be related to bedrock thermal expansion (they are correlated with temperature, but with a lag time of ~15-30 days), but this is not the case for the radial component (instead atmosphere/multipath?). Shorter-period signals are correlated with temperature, mainly for the east component and particularly for REP2 (the short-braced monument). We suspect this could be thermal expansion of the monument or upper ground layers (or both).

21. Thanks!

22. Rates REP3-REP2 (~90 m) (north) -0.07 ± 0.01 mm/yr REP3-REPO (~100 m) (east) 0.06 ± 0.01 mm/yr REP4-REP3 (~1 km) (north) -0.24 ± 0.01 mm/yr

23. Elevation-Angle Dependence Reduction in annual amplitude for the radial component with higher elevation angle cutoffs. Mean annual amplitude