Constraints on Shallow Low-Viscosity Earth Layers from Future GOCE Data

Transcription

1 Constraints on Shallow Low-Viscosity Earth Layers from Future GOCE Data Hugo Schotman 1,2, Bert Vermeersen 2, Pieter Visser rd International GOCE User Workshop, ESA Esrin, 7 November 2006

2 glacial-isostatic adjustment (GIA) lithosphere (elastic) asthenosphere (~10 21 Pas) crust mantle upper crust (elastic) lower crust (elastic or CLVZ*) * if lower crust has low viscosity (~ Pas) then: crustal low-viscosity zone (CLVZ) modified from Watts (2001)

3 forcings in GIA ICE-5G 1 ice heights at 21 kyrs BP resulting sea-level drop at 21 kyrs BP 1 Peltier (2004) Annu. Rev. Earth Planet. Sci.

4 predictions of GIA total GIA-induced geoid height remaining at present CLVZ-induced perturbations remaining at present

5 comparison with GOCE and GRACE geoid heights and perturbations sensitivity to properties CLVZ geoid height degree amplitude: c l =a!" m C lm 2 +S lm 2 a: radius Earth, C lm,s lm : dimensionless Stokes coefficients #: standard deviation of white noise on gravity gradients computed along a polar, 30-day repeat orbit properties of CLVZ v: viscosity [log Pas] t: thickness [km] d: depth [km]

6 problem definition main question: what can we learn on the structure and rheology of the shallow earth from high-resolution gravity data? approach: constrain properties of CLVZs in GIA models with GOCE data issues: effect of CLVZ is order of magnitude smaller than total GIA! look at perturbations: model with CLVZ minus background model (= same model without CLVZ) perturbations are sensitive to the ice and earth model CLVZs are laterally heterogeneous general steps: 1. extract perturbations using a global (laterally homogeneous) GIA model $ model errors 2. estimate properties of CLVZs from a regional (possibly laterally heterogeneous) GIA model here: synthetic data computed from a laterally homogeneous GIA model with a CLVZ, supplemented with recovery and GIA model errors

7 sensitivity to ice and earth model CLVZ-induced perturbations computed with different ice and/or earth models earth models VM2 (1) MI (2) lithosphere 120 km 98 km upper mantle 5e20 Pas 5e20 Pas lower mantle 3e21 Pas 5e21 Pas ice models ICE-5G (1) RSES (3) (1) Peltier (2004), Annu. Rev. Earth Planet. Sci. (2) Milne et al. (2004), J. Geophys. Res. (3) Lambeck et al., RSES, ANU Canberra

8 effect of lateral heterogeneities CLVZ-induced perturbations, no lateral heterogeneities difference between laterally heterogeneous* and homogeneous O E S *Shield, Extensional and Oceanic lithosphere, CLVZ only in E

9 extract perturbations geoid height degree amplitudes c l degree correlation coefficient % l synthetic data: [ICE-5G(VM2)+d20t10v18] (+[recovery error]) (+[model error]) (" m C lm V lm +S lm W lm ) % l =!(" m C lm 2 +S lm 2 )!(" m V lm 2 +W lm 2 ) true perturbations: [ICE- 5G(VM2)+d20t10v18] - [ICE-5G(VM2)]

10 higher viscosity CLVZ geoid height degree amplitudes degree correlation coefficient

11 model and recovery errors model error: difference between estimated* and true perturbations recovery error due to white noise (# = 3 me) on gravity gradients * filtering of synthetic data: l&30 $ (C lm,s lm )=0

12 prediction errors prediction error with recovery error (3mE) prediction error with recovery error (3mE) and model error prediction error:!["(p i d i ) 2 /N] (=rms misfit), p i : predictions, d i : data ref: reference CLVZ (d20t10v18)

13 conclusions CLVZ-induced perturbations are above performance of GOCE up to degree 150 (depends on properties CLVZ) perturbations due to CLVZs are sensitive to: properties CLVZ ice model earth model extracting perturbations from data results in model errors (due to filtering and/or uncertainties in ice and earth model) model errors are more severe than recovery errors prediction errors are minimal for reference CLVZ in presence of recovery and model errors (except for uncertainties in ice model)

14 outlook model errors due to other medium- to short-wavelength solid-earth features (related to e.g. subduction and passive margins) earth noise due to shallow density inhomogeneities further development of method (spatio-spectral techniques and formal inversion) further development of laterally heterogeneous earth model (e.g. compressibility, viscosity estimates from seismic data) use of existing gravity data (e.g. GRACE)