Characterizing the noise affecting land-based gravity measurements for improved distinction of tectonic signals Michel Van Camp Collaboration with: T.

Slides:



Advertisements
Similar presentations
It is very difficult to measure the small change in volume of the mercury. If the mercury had the shape of a sphere, the change in diameter would be very.
Advertisements

The quest for a consistent signal in ground and GRACE gravity time series M. Van Camp – B. Meurers – O. de Viron L. Métivier – O. Francis Special thanks:
Principles of the Global Positioning System Lecture 12 Prof. Thomas Herring Room A;
Jeffrey Walker Factors Affecting the Detection of a Soil Moisture Signal in Field Relative Gravity Measurements 1 Adam Smith, 1 Jeffrey Walker, 1 Andrew.
2006 AGU Fall Meeting. 14 Dec. 2006, San Francisco – Poster #G43A-0985 Jim Ray (NOAA/NGS), Tonie van Dam (U. Luxembourg), Zuheir Altamimi (IGN), Xavier.
Role of Space Geodesy In GEOSS Timothy H. Dixon University of Miami/RSMAS and Center for Southeastern Advanced Remote Sensing (CSTARS)
(Introduction to) Earthquake Energy Balance
An estimate of post-seismic gravity change caused by the 1960 Chile earthquake and comparison with GRACE gravity fields Y. Tanaka 1, 2, V. Klemann 2, K.
1 – Stress contributions 2 – Probabilistic approach 3 – Deformation transients Small earthquakes contribute as much as large earthquakes do to stress changes.
STAT 497 APPLIED TIME SERIES ANALYSIS
Limits of static processing in a dynamic environment Matt King, Newcastle University, UK.
“Real-time” Transient Detection Algorithms Dr. Kang Hyeun Ji, Thomas Herring MIT.
Long-Term Ambient Noise Statistics in the Gulf of Mexico Mark A. Snyder & Peter A. Orlin Naval Oceanographic Office Stennis Space Center, MS Anthony I.
03/09/2007 Earthquake of the Week
Principles of Sea Level Measurement Long-term tide gauge records  What is a tide station?  How is sea level measured relative to the land?  What types.
Correlation and spectral analysis Objective: –investigation of correlation structure of time series –identification of major harmonic components in time.
Principles of the Global Positioning System Lecture 11 Prof. Thomas Herring Room A;
Two and a half problems in homogenization of climate series concluding remarks to Daily Stew Ralf Lindau.
New Scientific Applications with Existing CGPS Capabilities Earthquakes, Soil Moisture, and Environmental Imaging Andria Bilich Geosciences Research Division.
1 Earthquake Magnitude Measurements for Puerto Rico Dariush Motazedian and Gail M. Atkinson.
Tectonic deformations inferred from absolute gravity measurements in Belgium and across the Roer Graben Michel Van Camp & Thierry Camelbeeck Royal Observatory.
Statistical Methods For Engineers ChE 477 (UO Lab) Larry Baxter & Stan Harding Brigham Young University.
Don P. Chambers Center for Space Research The University of Texas at Austin Understanding Sea-Level Rise and Variability 6-9 June, 2006 Paris, France The.
Respected Professor Kihyeon Cho
Handling Data and Figures of Merit Data comes in different formats time Histograms Lists But…. Can contain the same information about quality What is meant.
WE WA VI ST PE MO MB MC CO BH MC MB MO PE ST VI WA Comparison of GRACE gravity field solutions, hydrological models and time series of superconducting.
New earthquake category Nature 447, (3 May 2007) | doi: /nature05780; Received 8 December 2006; Accepted 26 March A scaling law for slow.
Dr. Richard Young Optronic Laboratories, Inc..  Uncertainty budgets are a growing requirement of measurements.  Multiple measurements are generally.
Error Analysis Accuracy Closeness to the true value Measurement Accuracy – determines the closeness of the measured value to the true value Instrument.
The Hunting of the SNARF Giovanni F. Sella Seth Stein Northwestern University Timothy H. Dixon University of Miami "What's the good of Mercator's North.
Chapter 8: The future geodetic reference frames Thomas Herring, Hans-Peter Plag, Jim Ray, Zuheir Altamimi.
Error Analysis Significant Figures and Error Propagation.
Define Problem Select Appropriate Methods Obtain and store sample Pre-treat sample Perform required measurements Compare results with standards Apply necessary.
SOES6002: Modelling in Environmental and Earth System Science Geophysical modelling Tim Henstock School of Ocean & Earth Science University of Southampton.
Secular variation in Germany from repeat station data and a recent global field model Monika Korte and Vincent Lesur Helmholtz Centre Potsdam, German Research.
Regional climate prediction comparisons via statistical upscaling and downscaling Peter Guttorp University of Washington Norwegian Computing Center
Deformation Analysis in the North American Plate’s Interior Calais E, Purdue University, West Lafayette, IN, Han JY,
Uncertainty in Uncertainty. Within a factor of 2.
Thoughts on the GIA Issue in SNARF Jim Davis & Tom Herring Input from and discussions with Mark Tamisiea, Jerry Mitrovica, and Glenn Milne.
An improved and extended GPS derived velocity field of the postglacial adjustment in Fennoscandia Martin Lidberg 1,3, Jan M. Johansson 1, Hans-Georg Scherneck.
Sub-Millimeter Tests of the Gravitational Inverse-Square Law C.D. Hoyle University of Washington In collaboration with: E.G. Adelberger J.H. Gundlach B.R.
RESULTS OF RESEARCH RELATED TO CHARIS IN KAZAKHSTAN I. Severskiy, L. Kogutenko.
GPS: “Where goeth thou” Thomas Herring With results from Jen Alltop: Geosystems Thesis Katy Quinn: Almost graduated Ph.D
Uncertainties for AH Phys. Accuracy and Precision The accuracy of a measurement tells you how close the measurement is to the “true” or accepted value.
Original objective = quantify intraplate deformation –Pros: Larger number of sites High density of sites in some areas Minimal cost… –Cons: Density varies.
Statistical Process Control04/03/961 What is Variation? Less Variation = Higher Quality.
Issues in GPS Error Analysis What are the sources of the errors ? How much of the error can we remove by better modeling ? Do we have enough information.
A. Güntner | Hydrogravimetry 1 Sub-humid climate (Mediterranean) Mean annual precipitation: 1200 mm, (highly seasonal) Elevation: 160 m amsl Early results.
Principles of the Global Positioning System Lecture 12 Prof. Thomas Herring Room ;
Attenuation measurement with all 4 frozen-in SPATS strings Justin Vandenbroucke Freija Descamps IceCube Collaboration Meeting, Utrecht, Netherlands September.
Issues in the Comparison of Ground Gravity with GRACE Data David Crossley, Saint Louis U., Dept. Earth & Atmospheric Science, 3507 Laclede Ave., St. Louis.
WE WA VI ST PE MO MB MC CO BH MC MB MO PE ST VI WA Comparison of GRACE gravity field solutions, hydrological models and time series of superconducting.
Don Chambers Center for Space Research, The University of Texas at Austin Josh Willis Jet Propulsion Laboratory, California Institute of Technology R.
HIGH FREQUENCY GROUND MOTION SCALING IN THE YUNNAN REGION W. Winston Chan, Multimax, Inc., Largo, MD W. Winston Chan, Multimax, Inc., Largo, MD Robert.
Error Modeling Thomas Herring Room ;
5/18/2994G21D-04 Spring AGU Realization of a Stable North America Reference Frame Thomas Herring Department of Earth Atmospheric and Planetary, Sciences,
A GPS-based view of New Madrid earthquake hazard Seth Stein, Northwestern University Uncertainties permit wide range (3X) of hazard models, some higher.
1 Rosalia Daví 1 Václav Vavryčuk 2 Elli-Maria Charalampidou 2 Grzegorz Kwiatek 1 Institute of Geophysics, Academy of Sciences, Praha 2 GFZ German Research.
Errors. Random Errors A random error is due to the effects of uncontrolled variables. These exhibit no pattern. These errors can cause measurements to.
The joint influence of break and noise variance on break detection Ralf Lindau & Victor Venema University of Bonn Germany.
Vertical velocities at tide gauges from a completely reprocessed global GPS network of stations: How well do they work? G. Wöppelmann 1, M-N. Bouin 2,
Aug 6, 2002APSG Irkutsk Contemporary Horizontal and Vertical Deformation of the Tien Shan Thomas Herring, Bradford H. Hager, Brendan Meade, Massachusetts.
Errors in Positioning Matt King, Newcastle University, UK.
Geodesy & Crustal Deformation
Geodesy & Crustal Deformation
7.3 Measuring and Predicting Earthquakes
Validation of mesoscale generalisation procedure for the WRF model
Principles of the Global Positioning System Lecture 11
by A. Dutton, A. E. Carlson, A. J. Long, G. A. Milne, P. U. Clark, R
Repeated gravity measurements across the Rhenish massif
Presentation transcript:

Characterizing the noise affecting land-based gravity measurements for improved distinction of tectonic signals Michel Van Camp Collaboration with: T. Camelbeeck (ROB) A. Dassargues (U. Liège) O. de Viron (IPGP) O. Francis (U. Luxembourg) H.-G. Scherneck (Chalmers) M. Van Clooster (UCL) S.D.P. Williams (Nat. Oceanography Centre) etc…

1.Known seismic activity: (a) present-day seismicity; (b) large historical earthquakes; 2.Geology + paleoseismology ; 3.Continuous GPS measurements ; years of dedicated geodetic experiments: (a) CGPS across the Feldbiss fault zone (Roer graben); (b) Absolute gravity. How is the ground moving in Northwestern Europe ?  Available information : 30 m in 300,000 yr

Strain rate and seismic activity (Lower Rhine Embayment) Paleoseismology, geology and historical seismicity agree: Total moment release ~ N.m/yr 350 km of active faults with an average slip rate around 0.1 mm/yr during the Late Pleistocene.  Measuring such a deformation rate: hopeless with geodesy?

Glaciation Deglaciation Peripheral bulge (43 to 55 °N) GIA effects on the peripheral bulge predicted by models based on GPS measurements in Fennoscandia : -0.9 mm/year in Belgium (Milne et al., 2001)  Presently not well estimated by geodetic measurements But not hopeless!  Absolute gravity measurements can help Strain rate and Glacial Isostatic Adjustment around 50°N (peripheral zone) ???

Repeated Absolute Gravity measurements: profile (for details see Van Camp et al., JGR, 2011)

The Membach Geodynamic Station AG: since 1996: 190 data  ~1 /month SG: continuously since 1995

Instrumental noise of AG and SG  Using AG to remove the SG drift  Difference [SG-AG]  AG setup noise  AG and SG spectra: power law noise:  High freq. (> 1 cpd): aliased AG data + instrumental noise >> important for the measurement protocol  Low freq. (< 1 cpd): >> important for geodetic studies

Drift of the superconducting gravimeter : Obtained by taking the difference [SG-AG] Exponential Linear t in years Half-life = 6.3 years SG is drifting (~35 nm/s²/yr): SG drift given by [SG-AG] (the AG does not drift) 190 AG measurements to drops  ~ 1 to 8 days

Causes of the SG exponential drift Drift is downward (g increases  sphere goes down)  Correction of steps? No :Should compensate each other or form a random-walk signal  Room temperature? No: Stable, and when major transient changes occurred (Dt = 4-5°C), no influence on g  Barometer? No: +0.5 hPa/yr  -1.7 nm/s²/yr: negligible here  Tiltmeters and thermal levellers? No: Sensitive to temperature changes but no correlation with g ; tilt null position successfully checked in 2006: same as in  Leak in the SG sensing unit  Temperature control inside the SG  Stability of the magnetic field  The capacitance bridge  Gas adsorption or desorption on the sphere  Tests to investigate actual causes are difficult, due to the required time (> 10 years !) Probably a combination of them for details see Van Camp & Francis, J. Geod. 2007

Drift-free superconducting gravity and absolute gravity data 40 nm/s² or 4 µGal 1 year Micro-g LaCoste

AG “Setup” noise: difference between SG and AG On 190 AG points [ ]:  nm/s² - 1  ≤ 66 % ≤ 1  - 2  ≤ 97 % ≤ 2  - 3  ≤ 98.5 % ≤ 3   AG Instrumental setup noise is white (but distribution +/- normal...depends on tests) Slightly more AG data are lower than SG: poor alignment of the verticality or the test and ref. beams, …  “setup noise” ~ 15 nm/s² (16 nm/s² in Van Camp et al., JGR 2005: based on 112 AG data only : we can keep this more conservative estimate)  Causes: height measurement, alignment, clock, floor coupling… Histogram for details see Van Camp et al., JGR 2005

Spectra of SG and AG time series at Membach ~ f -2.5 : power law noise ~ f -1.2 : fractional Brownian noise 10 days1 day 100 days 27 µGal d to d 7 µGal d to d 5 µGal d to d High microseismic noise : aliasing 0.08 µgal daily or 7 µGal drop to drop (10 s) 5 µGal d to d ???

AG noise at high frequencies (f > 1 cpd) at industrial and coastal stations PSD = 2 *  ² * T [(nm/s²)²/Hz] 0.08 Gal daily or 0.4 µGal hourly or 7 µGal drop to drop (10 s) 1 µGal daily or 4 µGal hourly or 75 µGal drop to drop Jülich noisy 1 / 5 s Jülich noisy 1 / 10 s (drop to drop ~ µGal) Jülich quiet 1 / 5 s Jülich quiet 1 / 10 s (drop to drop ~25 µGal) Ostend 1 / 10 s Ostend 1 / 5 s POL 1 / 10 s (average of 200 PSDs)

Usually: 1 drop / 10 s, 100 drops (some users work with 150 or 200 drops): More on the sampling rate: the case of Jülich One of the noisiest AG set we have ever recorded (in the absence of earthquakes) Standard deviation :  Experimental st. dev. of the mean :  /sqrt(N) Also called: “Measurement precision”

So, how to obtain valuable measurements at such a station? 1 drop / 10 s Increase sampling rate to reduce the aliasing effect: 1 drop/5 s, 200 drops/set

If white noise,  decreases as sqrt(N) : is the improvement just due to the number of drops (200 vs 100) ? No !  /2 1/2 = 25.9/1.4 = 18.3 µGal >< 5.8 µGal : we have much better! This is because we reduce the aliasing: The most important is increasing the sampling rate, not the number of data 100 drops/set or 200 drops/set1 ? 1 drop/5 s or 1 drop/10 s ? Summary: 1 drop/10 s: µGal; 100 drops/set   = 25.9 µGal ;  /sqrt(N) = 3.7 µGal 1 drop/5 s : µGal; 100 drops/set   = 6.8 µGal ;  /sqrt(N) = 1.0 µGal 1 drop/5 s : µGal; 200 drops/set   = 5.8 µGal ;  /sqrt(N) = 0.8 µGal 6.8/sqrt(2) = 4.8…not too bad: we have drop/5s, 200/set1 drop/5s, 100/set

Summary: reducing the aliasing : Example: the Jülich site 0.08 Gal daily or 0.4 µGal hourly or 7 µGal drop to drop (10 s) 1 µGal daily or 4 µGal hourly or 75 µGal drop to drop Not completely suppressed but much reduced using 1 drop/ 5 s

Summary: HF High noise : a problem ? 10 days No, provided that : - higher sampling rate and/or - longer measurement time Low microseismic noise : small enough to see the (white) instrumental noise ? 10 days1 day 100 days [Hz] No: at ~1 cpd geophysical noise dominates: HF noise not a problem, unless strong microseismic and industrial noise: then better to take 1 drop /5 s (for details see Van Camp et al., JGR, 2005)

Low frequency effects on repeated AG measurements (1/yr or 2/yr) Slow oscillations? Caused by hydrology? How can we explain these oscillations? 38.4  3.3 nm/s²/yr  ~19.4  1.6 mm/yr HF Noise not a problem, Rate similar to the expected ones in Fennoscandia or at plate boundaries

AG noise at low frequencies: power law processes Common for many type of geophysical signal  Effect on the estimated slope and the associated uncertainty !  = -2  f -2 : random walk (Brownian) First-order Gauss- Markov  = -1  f -1 : flicker f P(f) White noise AG (f > 1 cpd) 10 5 (nm/s²)²/Hz  min AnnualSemi-annual Flicker f Fractional f FOGM f -2 +white1714 Time (years) to measure a slope with an uncertainty of 1 nm/s²/yr (  0.5 mm/yr) Superconducting gravimeter 5 (nm/s²)²/Hz  s ???? (for details see Van Camp et al., JGR, 2005)

Does the power law process flatten at low frequency?  = -2  f -2 : random walk (Brownian) First-order, generalized Gauss-Markov  = -1  f -1 : flicker f P(f) White noise Does it flatten? How long does it take? Time (years) to measure a slope with an uncertainty of 2 nm/s²/yr (  1 mm/yr) ? (2  )  hydrology What is the cause of such a power-law noise?  hydrology

Correcting gravity (SG) using modelled water storage effects - Gravity changes predicted from the LaDworld-Gascoyne Land Water-Energy Balances model (1° x 1°, monthly) (Milly & Shmakin, ). Gravity before/after correcting the loading & Newtonian effects (Membach) nm/s² Worse Better Scatter in the gravity residuals: SG (raw): 15.6 nm/s² SG – Load – Newton:15.2 nm/s² Same problem (sometimes worse) in nearly all GGP stations (Boy & Hinderer, 2006, Van Camp et al., 2010)

PSDs LaD & SG in the time domain:  But LaD & SG similar in frequency domain : Power spectrum densities of SGs and LaD: black: SG (in the best case, since 1995) red : LaD (since 1980) Toward a flattening at periods > 1 year, for both SG and LaD  Hydrology follows a ”Generalized Gauss-Markov” behavior, which is included in the gravity signal 1 cpy Hydrology at longer periods: in the frequency domain Medicina (Italy) Sutherland (South Africa) Tigo (Chile) Van Camp et al., JGR cpy

Given the Generalized Gauss-Markov noise: StationTime (yr) Medicina3.1 Sutherland5.6 Wettzell10.1 Tigo16.7 Time necessary (years) to be able to measure a slope with an uncertainty of 2 nm/s² /yr (  ~ 1 mm/yr) (2  ), based on SG & LaD time series: 3 to 17 years < 5 yr < 10 yr < 15 yr > 15 yr Not contradicted by the profile: after 11 years : 2  ≈ nm/s² /yr Future: GLDAS model since 1948, taking ground water unto account (coming…)

Repeated AG measurements dg/dt resolved at the nm/s²/yr (95% confidence interval) after 11 years

Stability of repeated AG measurements  Gravity rate of change as a function of the length of the time series (Membach): 2  ~ 1 nm/s²/yr or 0.5 mm/yr after ~10 years

PSDs Hydrology: how to mitigate this? What you can do: 1)Like Jülich, Membach, Wettzell, Strasbourg...: Try to correct for local and large-scale effects (but I’m not so optimistic, not applicable everywhere) 2) Be patient : wait till hydrological signal averages zero. But how long ???  Investigating long superconducting gravimeter time series and predictions from LaD hydrological model (Milly & Schmakin): “HOW LONG”  < 15 years  Unless significant climate change, hydrology should not mask the GIA effect on the peripheral bulge.  Long AG time series may also be useful to investigate slow environmental changes !

Permanent GPS network Perspectives  Process the European GPS time series, + InSAR in the Roer Graben  Use the Absolute Gravity data as a constrain for the vertical component (see Teferle et al., GJI, 2009)  Necessity to improve GIA model to investigate other tectonic processes  Necessity to work on the (dg/dt)/(dz/dt) ratio

AG : Setup noise ~1.5 µGal; dominates the error budget of one AG value; When microseismic noise is low, instrumental (white) noise dominates, specific to each instrument; When the microseismic noise is high: clear aliasing effect : “easy” to reduce by increasing sampling rate... even in noisy stations such as Jülich (industrial) or Oostende (coastal), if measurements taken carefully; Uncertainty on the trend depends on the noise structure; If 2 measurements/yr: 2 nm/s²r [  1 mm/yr] (2  ) after 3-15 years if Generalized Gauss-Markov noise (flattens at low freq.). SG : Drift : for C021 exponential model to be preferred for records longer than 10 years (to be investigated for other SGs); SG great to monitor gravity between AG measurements; SG great as long period seismometer. Conclusions

That’s all Probably, discussing gravimetry