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J. Ebbing & N. Holzrichter – University of Kiel Johannes Bouman – DGFI Munich Ronny Stolz – IPHT Jena SPP Dynamic EarthPotsdam, 03/04 July 2014 Swarm &

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1 J. Ebbing & N. Holzrichter – University of Kiel Johannes Bouman – DGFI Munich Ronny Stolz – IPHT Jena SPP Dynamic EarthPotsdam, 03/04 July 2014 Swarm & GOCE to reveal the dynamic and static coupling within the lithosphere

2 Modelling Concept Crust Moho LAB Lithospheric mantle Asthenospheric mantle 1315°C

3 Moho LAB Lithospheric mantle Asthenospheric mantle 1315°C Crust SWARM 580°C Curie temperature isotherm

4 Moho LAB Lithospheric mantle Asthenospheric mantle 1315°C Crust SWARM 580°C Curie temperature isothermΔρ Gravity gradients (GOCE)

5 Moho (GOCE,GRACE) Δρ GOCE gradients ρ(T,C) GOCE gradients ρ(T,C) Δρ Gravity gradients (GOCE) LAB Lithospheric mantle Asthenospheric mantle 1315°C Crust 580°C Curie temperature isotherm SWARM Gravity, Geoid ρ(T,C) Gravity, Geoid ρ(T,C)

6 GOCE data @ satellite altitude / Earth’s surface Signal @ satellite altitude is smooth Downward continuation enhances signal power & details

7 Saudi Arabia Height 0 km10 km260 km Signal RMS 5.1 E4.1 E0.3 E Model error 1.3 E0.9 E0.4 mE Omission error 83.5 E8.1 E0.2 mE V ZZ degree RMS h = 0 & 260 km Downward continuation also amplifies noise Effective resolution of data does not change Omission error becomes much larger (mainly high frequency topography) Saudi Arabia Height 0 km10 km260 km Signal RMS 5.1 E4.1 E0.3 E GOCO03S V ZZ signal & error, L = 225 For model inversion it is probably best to use data close to their original point of acquisition. GOCE data @ satellite altitude / Earth’s surface

8 Inversion: Gz >90 km Z 0 =30 km  = 350 kg/m 3 Moho depth by gravity inversion

9 - satellite residuals - Inversion: Gz >90 km Z 0 =30 km  = 350 kg/m 3

10 Inversion Gzz=Full Z 0 =30 km  = 350 kg/m 3 Moho depth by satellite gravity gradient inversion

11 Sensitivity of satellite gradients Z. Martinec 2013 Sensitivity kernels for spherical gravity gradients

12 Improvement of Lithospheric Field Model... with present satellites Ørsted and CHAMP... N = 60, resolution: 670 km... and with Swarm N = 133, resolution: 300 km Magnetic field of Earth’s crust radial component at 10 km altitude Before Ørsted... N = 30, resolution: 1330 km

13 Poisson’s relation Magnetization of a tesseroid. The formula to calculate gravity gradient tensor of a spherical prism (Asgharzadeh et al., 2007), along with adaptive integration method (Li et al., 2011) was used in software package called tesseroids- 1.1 (Uieda et al., 2011; Uieda, 2013). By Poisson’s relation (Blakely, 1995) the magnetic field is mathematically equivalent to the gradient of a gravity field. Therefore, tesseroids was modified to calculate a magnetic field. To this end the Earth crust is modelled by spherical prisms with prescribed magnetic susceptibility and remanent magnetization. Induced magnetizations are then derived from product of the chosen main field model (such as International Geomagnetic Reference Field) and the corresponding tesseroid susceptibilities. Remanent magnetization vectors are directly set. Spherical modelling tools

14 Numerical methods in comparison (Baykiev 2014) TesseroidsSpherical caps Input: Crust1.0 Susceptibility of the whole crust – 0.04 SI Ambient field – IGRF11 Grid resolution – 2x2 deg Grid altitude – 400 km (also in Purucker et al. 2002)

15 Interpretation of magnetic anomalies Gradients along track can be recovered from Swarm data (Kotsiaros Olsen 2013) Invariants of gravity and magnetic field will help on polar regions to avoid coordinate system dependency => Normalized source strength can be used to describe lithospheric magnetization

16 A study area Why Greenland? Little geophysical data available Mass estimates of changing ice sheets necessary for climate models Coupling with physical state of lithosphere essential to estimate dynamic behaviour Role of Iceland hotspot track?

17 Summary Analysis of satellite gravity data: lithospheric structure Ice thickness vs. crustal thickness dynamic vs. static components Analysis of satellite magnetic data: Characterization of magnetic crustal thickness Normalized source strength for describing tectonic domains  Interpretation with DTU & GEUS  Implications for rheology Technical challenges: Magnetic gradients along the track (with DTU) Tesseroids for complete magnetic tensor modelling (with NGU) Implementation of invariant analysis in inverse and forward modelling

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