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WESTERN BALKANS MOHO DEPTH AND CRUSTAL STRUCTURE EXPLOITING GOCE DATA D. SAMPIETRO(1), M. REGUZZONI(1,2) (1) GReD s.r.l. – Geomatics Research & (2) Department of Civil and Development Environmental Engineering – Politecnico Spin-off del Politecnico di Milano di Milano http://www.g-red.eu [email protected] GOCE user workshop– Paris, 26/11/2014 http:// http://www.dica.polimi.it/ 5° International THE WESTERN BALKAN AREA While the crustal structure of the Pannonian, Transilvanian, Adriatic, and Carpathian basins is reasonably well know thanks to the exploration of oil companies the Dinarides and the surrounding areas suffer from lack of measurements, which results in high uncertainties in the estimations of the Moho depths. Well known provinces Raykova, R., & Nikolova, S. (2007). Studia Geophysica et Geodaetica, 51(1), 165-184. In the present work we will study the Moho depth of the Western Balkan Region by exploiting GOCE data. 5° International GOCE user workshop– Paris, 26/11/2014 THE INVERSION PROCEDURE Sediment, ocean, and ice models GOCE data Crystalline crust model Data stripping Reduced data Mantle effect DATA REDUCTION Geological provinces class. The Inversion procedure is composed by two main steps the data reduction and the inversion of the residual field. GOCE data are used in both steps: • In the former they are used to classify the area in geological homogeneous patches; Seismic combination Linearizati on point INVERSION Δρ Inversion operator Density functions • In the latter they are used as observation to apply the inversion algorithm. D ρ Convergence ? yes ρ CRUSTAL no MODEL D 5° International GOCE user workshop– Paris, 26/11/2014 STEP 1: DATA REDUCTION Removed with geometry from ETOPO1 and fixed density. Removed with geometry from ETOPO1 and fixed density. Crystalline crust represents the most important contribute. Its density defines the gravitational effect to be removed and the density contrast at the Moho discontinuity. Unknown geometry, density from crystalline crust and upper mantle models. Removed with geometry defined by the mean Moho depth and density from GyPSuM model (Simmons et al. Journal of Geophysical Research: Solid Earth 2010) Removed with geometry and density from Laske & Masters, EOS Trans. AGU, 78, F483, 1997). 5° International GOCE user workshop– Paris, 26/11/2014 THE IMPORTANCE OF GEOLOGICAL PROVINCES FOR MOHO DEPTH DETERMINATION 𝜌=𝑎+ b 𝑉𝑝 Christensen and Mooney, J GEOPHYS RES, 100(B7), PP. 9768, JUNE 10, 1995. Note that the definition of the geological provinces is crucial to correctly reduce the data and invert the residual signal. 5° International GOCE user workshop– Paris, 26/11/2014 THE IMPORTANCE OF GEOLOGICAL PROVINCES FOR MOHO DEPTH DETERMINATION [KM] GEMMA1.0[1] [1] M. Reguzzoni, D. Sampietro. GEMMA: An Earth crustal model based on GOCE satellite data, Int J Appl Earth Obs, doi:10.1016/j.jag.2014.04.002 5° International GOCE user workshop– Paris, 26/11/2014 BAYESIAN ESTIMATION OF GEOLOGICAL PROVINCES BOUNDARIES We suppose to have a rough geological provinces model and that the geological province Gi of a pixel i can be either the apriori one or the ones of its neighborhood Δi. THE PRIOR probability of Gi is computed by defining a weight-matrix W, e.g. the probability that the geological province Gi is G1 is equal to: P Gi G1 j i ,i Geologic Province and Thermo-Tectonic Age Maps (Exxon, Technical Report, 1995) . 1jW j where: 1j 1 G j G1 if 1 1 0 G G j j e.g. P Gi1 2 P Gi2 2 3 5° International GOCE user workshop– Paris, 26/11/2014 BAYESIAN ESTIMATION OF GEOLOGICAL PROVINCES BOUNDARIES THE LIKELIHOOD is defined by supposing an isostatic Moho depth (Airy model) di hi c ,i m,i c ,i 1 in planar approximation (with hi topography) and considering at each pixel the gravitational effect of a Bouguer plate of thickness ti hi di , so that: gi 2c ,iti . Under these assumptions an approximated relation between ρc,i and δgi holds: 𝜌𝑐,𝑖 = 𝑓 𝛿𝑔𝑖 The likelihood is supposed to be normally distributed with a mean given by 𝝁𝜌𝑐 𝑮𝓵 and a standard deviation 𝝈𝜌𝑐 𝑮𝓵 Where 𝜇𝜌𝑐 𝐺 𝓁 is the mean value of 𝜌𝑐,𝑖 𝐺 𝓁 computed for the geological province 𝐺 𝓁 and 𝜎𝜌𝑐 𝐺 𝓁 is its standard deviation. 5° International GOCE user workshop– Paris, 26/11/2014 BAYESIAN ESTIMATION OF GEOLOGICAL PROVINCES BOUNDARIES 𝜌𝑐,𝑖 [kg/m3] GOCE gravity disturbances 𝛿𝑔 Approximated density variation used for the Bayesian classification 5° International GOCE user workshop– Paris, 26/11/2014 [m/s2] BAYESIAN ESTIMATION OF GEOLOGICAL PROVINCES BOUNDARIES THE POSTERIOR distribution of Gi is computed by applying the well known Bayes theorem. Finally the geological province of the pixel is chosen by maximizing the posterior distribution (MAP). INITIAL MODEL ADJUSTED MODEL MAX OF THE POSTERIOR PROBABILITY Bada et al. (1998). Geophysical Journal International, 134(1), 87-101. The algorithm allows to compute also the probability that a pixel belongs to a geological province 5° International GOCE user workshop– Paris, 26/11/2014 STEP 2: INVERSION Basically the solution is based on the same procedure developed to compute the GEMMA1.0 global model but the global inversion in terms of spherical harmonic is here substituted by the regional inversion in terms of Wiener deconvolution. The Inversion procedure allows: - to estimate the mean Moho depth even once the normal field is removed; - to take into account the crustal density variation in the radial direction; - to correct the a-priori density for scale factors; 5° International GOCE user workshop– Paris, 26/11/2014 STEP 2: INVERSION Reduced GOCE data GEMMA1.0 model Local seismic model Signal Power spectrum Average Moho depth for each geological province Linearization 𝛿𝑔 𝑥 = 𝑘 𝑥 − 𝜉 𝛿𝐷 𝜉 Δ𝜌 𝜉 𝑑𝜉 𝑘=𝑘 𝐷 Crustal model Fast Fourier Transform Wiener Filter no Δρ, δD Seismic combination Convergence ? Δρ yes Final model 5° International GOCE user workshop– Paris, 26/11/2014 RESULTS LOCAL MOHO MODEL FROM SEISMIC OBSERVATIONS CRUST 1.0 LOCAL SOLUTION ESC MOHO GEMMA1.0 5° International GOCE user workshop– Paris, 26/11/2014 [KM] RESULTS LOCAL MOHO MODEL FROM SEISMIC OBSERVATIONS CRUST 1.0 LOCAL SOLUTION Difference Mean [km] Std [km] Crust 1.0 -2.3 2.6 ESC -5.3 2.9 GEMMA 1.0 -0.1 2.5 Local Solution -0.7 0.9 ESC MOHO GEMMA1.0 5° International GOCE user workshop– Paris, 26/11/2014 [KM] CONCLUSIONS In the present work an algorithm to refine the shape of the main geological provinces driven by GOCE data in a Bayesian scheme has been studied and implemented. First tests (on real data) seem to prove the reliability of this Bayesian method thus encouraging possible applications of GOCE observations in this field. An improved procedure to estimate Moho depth from GOCE data at local scale has been studied and implemented. Results in terms of geological provinces modelling and Moho depth estimation shown the reliability of the method giving results comparable with those obtained from seismic profiles. The use of gravity gradients can improve the geological provinces modelling. This should be investigated in future studies 5° International GOCE user workshop– Paris, 26/11/2014 THANKS FOR YOUR ATTENTION GReD s.r.l. – Geomatics Research & Department of Civil and Development Environmental Engineering – Politecnico Spin-off del Politecnico di Milano di Milano http://www.g-red.eu [email protected] GOCE user workshop– Paris, 26/11/2014 http:// http://www.dica.polimi.it/ 5° International