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RESEARCH PRODUCT

Inversion of gravity anomalies over spreading oceanic ridges

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Abstract Models of spreading ocean ridges are derived by Bayesian gravity inversion with geophysical and geodynamic a priori information. The aim is to investigate the influence of spreading rate, plate dynamics and tectonic framework on crust and upper mantle structure by comparing the Mid Atlantic Ridge (MAR), the Indian Ocean Ridge (IND) and the East Pacific Rise (PAC). They differ in mean spreading rate, dynamic settings, as attached slabs, and plume interaction. Topography or bathymetry, gravity, isostasy, seismology and geology, etc. are averaged along the ridges and guide the construction of initial 2D models, including features as mean plumes, i.e. averaged along the ridge. This is a gross simplification, and the results are considered preliminary. Three model types are tested: (a) the temperature anomaly; (b) asthenospheric rise into thickening lithosphere; (c) a crustal root as had been anticipated before seafloor spreading was discovered. Additional model components are a mean plume, a non-compensated ridge uplift, an under-compensated asthenospheric rise, e.g. of partially molten material, and seismic velocity models for P and S waves. Model type (c), tends to permute to model type (b) from thick crust to thin axial lithosphere. Model type (a) renders ‘realistic’ values of the thermal expansivity, but is insufficient to fit the gravity data; partial melt may disturb the simple temperature effect. A combination of (a) and (b) is most adequate. Exclusive seismic velocity models of S or P waves do not lead to acceptable densities nor to adequate gravity fitting. The different ridges exhibit significant differences in the best models: ATL and IND show an axial mass excess fostering enhanced ridge push, and ATL, in addition, suggests a mean plume input, while PAC shows an axial mass deficit reducing ridge push, most probably due to dominance of slab pull in the force balance. Goodness of the gravity fit alone is no justifiable criterion for goodness of model, indeed minor modifications to each model within the uncertainties of the assumptions can make the fit arbitrarily good. Goodness of model is quantified exclusively by a priori information.