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GOCE GRADIENT TENSOR CHARACTERIZATION OF THE COUPLED PARANÁ (SOUTH AMERICA) AND ETENDEKA (AFRICA) MAGMATIC PROVINCES Patrizia Mariani and Carla Braitenberg Department of Mathematics and Geoscience, University of Trieste
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Motivation Studying the first step of continental breakup of Paraná and Etendeka (South America and Africa) region. Looking for heterogeneous lithosphere explainig asymmetric volcanic effusion.
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Introduction oceanic crust seismics results: asymmetry of the magmatic intrusion into the margins. How is the lithosphere affected by continental breakup? Red: magmatism 150-5Ma Uenzelmann-Neben, Nat.Geosc., 2014
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Methodology GOCE gradients for SAM-AFR conjugate margins investigation GOCETIMR5 GOCE TIMR5 (Pail et al. 2011) Two different heights: 10 and 250 km. Other geophysical data: seismic tomography, seismics, bore-hole logging data, petrological models
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GOCE signal SAMAFRICA
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Statistics of Trr MinMaxMeanSTD SAM 10km-27.717.40.12.9 ETAN 10km-8.28.90.32.3 SAM 250km-0.50.9-0.10.2 ETAN 250km-0.30.70.10.2 Units: E.U.
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PARANÁ (SAM) Database ETOPO1 ISOPACHS: Merged dataset, continental is from local and PLATES database, oceanic is from NOAA. MOHO: Seismological model (Assumpção et al. 2013) MANTLE: Tomographic model (Simmons et al. 2012).
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ETENDEKA (Africa) Database ETOPO1 ISOPACHS: Merged dataset, continental is from (Milesi et al. 2013), oceanic is from NOAA, and PLATES project MOHO: Seismological model (Tugume et al. 2013; Airy root) MANTLE: Tomographic model (Simmons et al. 2012).
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Isopachs modeling: Testing dataset ETAN (Etendeka-Angola) Milesi et al. vs PLATES Offshore: NOAA vs PLATES Offshore logging data are used to constrain density Paraná (SAM) Merge PLATES with Mariani et al. 2013 dataset Subdivide isopachs with age criterium, corresponding to different density.
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MOHO MODELING & ISOSTATIC ROOT SAM : Seismological Moho model Assumpção et al. (2013); ETAN: Integrated model of Tugume et al. (2014), Crust1, isostatic modeling. Make comparison with seismological model or seismic data, also integrated models are checked Testing different parameter of reference depth 35 -30 km Density: -400, -500 kg/m 3
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Tzz VALUES FOR ISOPACHS MODELMin Max 10 km Min Max 250 km MILESI-2.40.5-0.20.01 NOAA-119.5-0.50.03 PLATES-7.75.9-1.40.2 MODELMin Max 10 km Min Max 250 km PRE- VULC -8.53.90.2 VULC-0.91.20 POST- VULC -185.8-0.40.2 NOAA-12.35.30.90.1 PARANA’ETENDEKA
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STATISTICAL VALUES FOR MOHO The contrast density of 100 kg/m3 at 10 km is 1.3 E.U., at 250 km is 0.5 E.U. The variation of 5 km of depth change at 10 km of 0.4-0.3, at 250 km 0.1 E.U. The range of the effect at 10 km about -17 to 26 E.U. At 250 km is about -5 to 5 E.U. ModelMinMaxMeanSTD 35_500 10km-19.726.626.6 35_400 10km-15.7211.65.3 30_500 10km-20.822.60.96.2 30_400 10km-16.618.10.75.0 35_500 250km-65.60.92.7 35_400 250km-4.84.50.72.2 30_500 250km-6.94.70.12.7 30_400 250km-5.53.80.12.1 STD: H=10km 6E.U. ; H=250 3 E.U.
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Moho depth in SAM Comparison Airy effect –Seismological Moho Seismological moho and Isostatic root
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Moho depth in Etendeka Comparison Airy effect –Seismological Moho Comparison between Seismological and Seismic moho
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Study of the Upper mantle Information about the mantle composition are given by: 1) Geophysical studies 2) Mantle xenolites 3) Ophiolites: uplifted oceanic crust + upper mantle Pay attention to density most important parameter during the Tzz calculations
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Mantle Modeling Several models are tested. Linear Temperature Gradient depending on last orogenic episode Changing Mineralogical Composition, constrained by T, P depth variation, age dependent
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Linear Temperature The T change from top to bottom (1300°C, according to Artemieva 2009) Tzz value for the maximum grid: Archean_10km: -0.5;1.2; STD:0.2 E.U. Archean_250km: -0.04; 0.3; STD 0.07 E.U. A=Archean; P=Proterozoic, AR=ActiveRegion
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Mantle Petrological classification Age influence oncomposition (Artemieva 2009) Proportion of lead minerals Olivine/Ortopiroxenite/Clinopiroxine/Garnet
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Our modeling Variation of elastic parameters, thermal expansion in function of T and P for different rocks. Bulk velocity from average of components (Hacker et al., 2003) Model predicts also bulk density Predict mantle rock parameters Validation with seismic tomography ρ Vp
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Calculation density profile for mantle Density modeling with method Hacker et al., 2003, JGR
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MANTLE MODELING Seismic tomographic model from Simmons et al. (2012) for lithosphere thickness Velocity from tomography conversion density with literature relations (compositional variation)
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P-wave Velocity profile (SAM)
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Tzz Residual calculation in PARANÁ
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Tzz Residual calculation in ETENDEKA WITH PLATES ISOPACHS MODELWITH Milesi et al. 2013 and NOAA
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Statistics of Residual Trr MinMaxMeanSTD SAM 10km -12.817.50.13.4 ETAN_NM10km-9.314.5-0.12.4 ETAN_P 10km -6.17.70.12.6 SAM 250km -2.82.8-0.11.1 ETAN_NM250km-0.50.4-0.10.2 ETAN_P 250km -0.61.10.10.4
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Tzz Forward & Gravity Inversion modeling in ETENDEKA … But anomaly continues along the coast 6 km thick 1 ° in longitude 2.5 ° in latitude Above the Moho (top -23 bottom -29km) Angola Namibia
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Discussion In Etendeka: Namibia is volcanic margin with associated underplating, while Angola margin is not volcanic, and has no underplated material but serpentinized mantle or lower crust (Contrucci et al. 2004). But: no loss of continuity in Tzz high. Inversion gives density high at base of crust. The modeling assumption is relevant to final interpretation: see differences in Etendeka between PLATES, Milesi and NOAA.
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Conclusion Etendeka-Parana’ can be well studied with GOCE products Parana’ and Etendeka show both increased density lower crust. Required for explaining the gravity and gradients. In Etendeka: Namibia and Angola classified as different type of margins respect to volcanism- does not fit continuous Tzz observation. Densification is continuous and probably tied to LIP.
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Outlook Refine and integrate the mantle effect Fix problem with Archean mantle Tzz inversion program End of PERLA project
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Thank you for your attention
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