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Ítem 3-D structure of the Rio Grande Rift from 1-D constrained joint inversion of receiver functions and surface wave dispersion(Elsevier, 2014-01-01) Romero, RodrigoThe Southern terminus of the Rio Grande Rift region has been poorly defined in the geologic record, with few seismic studies that provide information on the deeper Rift structure. In consequence, important questions related to tectonic and lithospheric activity of the Rio Grande Rift remain unresolved. To address some of these geological questions, we collect and analyze seismic data from 147 EarthScope Transportable Array (USArray) and other seismic stations in the region, to develop a 3-D crust and upper mantle velocity model. We apply a constrained optimization approach for joint inversion of surface wave and receiver functions using seismic S wave velocities as a model parameter. In particular, we compute receiver functions stacks based on ray parameter, and invert them jointly with collected surface wave group velocity dispersion observations. The inversions estimate 1-D seismic S-wave velocity profiles to 300 km depth, which are then interpolated to a 3-D velocity model using a Bayesian kriging scheme. Our 3-D models show a thin lower velocity crust anomaly along the southeastern Rio Grande Rift, a persistent low velocity anomaly underneath the Colorado Plateau and Basin and Range province, and another one at depth beneath the Jemez lineament, and the southern RGR.Ítem Constrained optimization framework for joint inversion of geophysical data sets(2013-09-17) Sosa Aguirre, Uram AnibalMany experimental techniques in geophysics advance the understanding of Earth processes by estimating and interpreting Earth structure (e.g. velocity and/or density structure). Different types of geophysical data can be collected and analysed separately, sometimes resulting in inconsistent models of the Earth depending on the data used. We present a constrained optimization approach for a joint inversion least-squares (LSQ) algorithm to characterize 1-D Earth's structure. We use two geophysical data sets sensitive to shear velocities: receiver function and surface wave dispersion velocity observations. We study the use of bound constraints on the regularized inverse problem, which are more physical than the regularization parameters required by conventional unconstrained formulations. Specifically, we develop a constrained optimization formulation that is solved with a primal-dual interior-point (PDIP) method, and validate our results with a traditional, unconstrained formulation that is solved with a truncated singular value decomposition (TSVD) for a set of numerical experiments with synthetic crustal velocity models. We conclude that the PDIP results are as accurate as those from the regularized TSVD approach, are less affected by noise, and honour the geophysical constraints. © The Authors 2013. Published by Oxford University Press on behalf of The Royal Astronomical Society.