Western Balkans Moho Depth and Crustal Structure Exploiting GOCE Data D. Sampietro(1), M. Reguzzoni(1,2) (1) GReD s.r.l. – Geomatics Research & Development Spin-off del Politecnico di Milano http://www.g-red.eu info@g-red.eu (2) Department of Civil and Environmental Engineering – Politecnico di Milano http:// http://www.dica.polimi.it/

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. The Western Balkan area, basically the region laying between Bulgaria and the Adriatic Sea, is one of the most complex and active European tectonic setting. The area is located in the correspondence of the collision between the African and Eurasian plates and is characterized by the of the Alpine-Himalayan orogenic Belt and by the opening of the Pannpresence onian Basin see Fig. 1. In particular, due to the thrusting of the Adria Plate into the European lithosphere, the Alps,the Dinarics and the Albanides was formed. According to latest studies, mainly based on the analysis of velocities retrieve from permanent GNSS networks, and on passive seismic methods Adria is now seen as a set of two or even three smaller units moving northward with velocity of the order of 35 mm/year (Oldow et al. 2002; Herak et al. 2005).On the contrary the Southern Carpathian and East part of Balkan Peninsula show southward oriented movement with magnitude of about 3 mm/year.

Geological provinces class. The Inversion Procedure 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; In the latter they are used as observation to apply the inversion algorithm. Sediment, ocean, and ice models GOCE data Geological provinces class. Crystalline crust model Data stripping Data reduction Reduced data Mantle effect Seismic combination Inversion Δρ Linearization point Inversion operator Density functions D ρ D Convergence? model Crustal no yes ρ

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 and density from Laske & Masters, EOS Trans. AGU, 78, F483, 1997). Removed with geometry defined by the mean Moho depth and density from GyPSuM model (Simmons et al. Journal of Geophysical Research: Solid Earth 2010)

The Importance of Geological Provinces for Moho Depth Determination Christensen and Mooney, J GEOPHYS RES, 100(B7), PP. 9768, JUNE 10, 1995. 𝜌=𝑎+ b 𝑉 𝑝 Note that the definition of the geological provinces is crucial to correctly reduce the data and invert the residual signal.

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

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 a-priori 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: where: Geologic Province and Thermo-Tectonic Age Maps (Exxon, Technical Report, 1995) . e.g.

Bayesian Estimation of Geological Provinces Boundaries THE LIKELIHOOD is defined by supposing an isostatic Moho depth (Airy model) in planar approximation (with hi topography) and considering at each pixel the gravitational effect of a Bouguer plate of thickness , so that: Under these assumptions an approximated relation between ρc,i and δgi holds: 𝜌 𝑐,𝑖 =𝑓 𝛿𝑔 𝑖 The likelihood is supposed to be normally distributed with a mean given by 𝝁 𝜌 𝑐 𝑮 𝓵 and a standard deviation 𝝈 𝜌 𝑐 𝑮 𝓵 Where 𝜇 𝜌 𝑐 𝐺 𝓁 is the mean value of 𝜌 𝑐,𝑖 𝐺 𝓁 computed for the geological province 𝐺 𝓁 and 𝜎 𝜌 𝑐 𝐺 𝓁 is its standard deviation.

Bayesian Estimation of Geological Provinces Boundaries 𝜌 𝑐,𝑖 [kg/m3] GOCE gravity disturbances [m/s2] 𝛿𝑔 Approximated density variation used for the Bayesian classification

Max of the posterior probability 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 Bada et al. (1998). Geophysical Journal International, 134(1), 87-101. Max of the posterior probability The algorithm allows to compute also the probability that a pixel belongs to a geological province

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;

Step 2: Inversion no yes Reduced GOCE data GEMMA1.0 model Fast Fourier Transform Linearization 𝛿𝑔 𝑥 = 𝑘 𝑥 − 𝜉 𝛿𝐷 𝜉 Δ𝜌 𝜉 𝑑 𝜉 𝑘=𝑘 𝐷 GEMMA1.0 model Wiener Filter Signal Power spectrum Local seismic model Average Moho depth for each geological province Seismic combination Convergence? Crustal model Δρ, δD no Δρ yes Final model

Results [km] Local Moho model from seismic observations Local solution Crust 1.0 ESC Moho GEMMA1.0 Moho depth from seismic observation developed in the framework of IUGG upper mantle program.

Results [km] Difference Mean [km] Std Crust 1.0 -2.3 2.6 ESC -5.3 2.9 Local Moho model from seismic observations [km] Local solution Difference Mean [km] Std 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 Crust 1.0 ESC Moho GEMMA1.0 Moho depth from seismic observation developed in the framework of IUGG upper mantle program.

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

Thanks for your attention GReD s.r.l. – Geomatics Research & Development Spin-off del Politecnico di Milano http://www.g-red.eu info@g-red.eu Department of Civil and Environmental Engineering – Politecnico di Milano http:// http://www.dica.polimi.it/