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

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.

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

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

GOCE signal SAMAFRICA

Statistics of Trr MinMaxMeanSTD SAM 10km ETAN 10km SAM 250km ETAN 250km Units: E.U.

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).

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).

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 dataset  Subdivide isopachs with age criterium, corresponding to different density.

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 km  Density: -400, -500 kg/m 3

Tzz VALUES FOR ISOPACHS MODELMin Max 10 km Min Max 250 km MILESI NOAA PLATES MODELMin Max 10 km Min Max 250 km PRE- VULC VULC POST- VULC NOAA PARANA’ETENDEKA

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 , 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 _400 10km _500 10km _400 10km _ km _ km _ km _ km STD: H=10km 6E.U. ; H=250 3 E.U.

Moho depth in SAM Comparison Airy effect –Seismological Moho Seismological moho and Isostatic root

Moho depth in Etendeka Comparison Airy effect –Seismological Moho Comparison between Seismological and Seismic moho

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

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

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

Mantle Petrological classification Age influence oncomposition (Artemieva 2009) Proportion of lead minerals Olivine/Ortopiroxenite/Clinopiroxine/Garnet

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

Calculation density profile for mantle Density modeling with method Hacker et al., 2003, JGR

MANTLE MODELING  Seismic tomographic model from Simmons et al. (2012) for lithosphere thickness  Velocity from tomography  conversion density with literature relations (compositional variation)

P-wave Velocity profile (SAM)

Tzz Residual calculation in PARANÁ

Tzz Residual calculation in ETENDEKA WITH PLATES ISOPACHS MODELWITH Milesi et al and NOAA

Statistics of Residual Trr MinMaxMeanSTD SAM 10km ETAN_NM10km ETAN_P 10km SAM 250km ETAN_NM250km ETAN_P 250km

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

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.

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.

Outlook  Refine and integrate the mantle effect  Fix problem with Archean mantle  Tzz inversion program  End of PERLA project

Thank you for your attention