Joint inversion of surface wave velocity and gravity observations and its application to central Asian basins shear velocity structure

Abstract

[1] We implement and apply a method to the jointly inverted of surface wave group velocities and gravity anomalies observations. Surface wave dispersion measurements are sensitive to seismic shear wave velocities, and the gravity measurements supply constraints on rock density variations. Our goal is to obtain a self-consistent three-dimensional shear velocity-density model with increased resolution of shallow geologic structures. We apply the method to investigate the structure of the crust and upper mantle beneath two large central Asian sedimentary basins: the Tarim and Junggar. The basins have thick sediment sections that produce substantial regional gravity variations (up to several hundred milligals). We used gravity observations extracted from the global gravity model derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. We combine the gravity anomalies with high-resolution surface wave slowness tomographic maps that provide group velocity dispersion values in the period range between 8 and 100 s for a grid of locations across central Asia. To integrate these data, we use a relationship between seismic velocity and density constructed through the combination of two empirical relations. One determined by Nafe and Drake, most appropriate for sedimentary rocks, and a linear Birch's law, more applicable to denser rocks (the basement). An iterative, damped least squares inversion including smoothing is used to jointly model both data sets, using shear velocity variations as the primary model parameters. Results show high upper mantle shear velocities beneath the Tarim basin and suggest differences in lower crust and upper mantle shear velocities between the eastern and western Tarim.Citation: Maceira, M., and C. J. Ammon (2009), Joint inversion of surface wave velocity and gravity observations and its application to central Asian basins shear velocity structure,

Seismic tomographic imaging of the crust and upper mantle under the central and western Tien Shan orogenic belt

“…For example, under the Tarim and Fergana basins, some prominent low‐ Vp , low‐ Vs and high‐ σ anomalies are revealed at shallow depths and are underlain by high‐ Vp , high‐ Vs and low‐ σ anomalies at greater depths (Figures 11–13), which is consistent with those inferred by previous studies [e.g., Ghose et al , 1998; Kosarev et al , 1993; Roecker et al , 1993; Maceira and Ammon , 2009; Omuralieva et al , 2009], although the Tarim basin is located on the margins of the study region. These low‐ V and high‐ σ anomalies under the basins at shallow depths may suggest the existence of the less‐compacted Cenozoic sediments due to erosion and denudation from the surrounding mountains [e.g., Ghose et al , 1993; Roecker et al , 1993; Maceira and Ammon , 2009; Omuralieva et al , 2009; Zheng et al , 2010], while high‐ V and low‐ σ anomalies under the basins at greater depths may suggest the existence of craton‐like strong lithospheric material. Such a structural feature under the Tarim basin may support the fact that the stress can be effectively transferred to the Tien Shan orogenic belt from the India‐Asia collision front [e.g., England and Houseman , 1985].…”

High resolution 3-D crustal structure beneath NE China from joint inversion of ambient noise and receiver functions using NECESSArray data

“…The c 1 and c 2 are weights for two types of data. Following Julià et al (2000) and Maceira and Ammon (2009), the c 1 and c 2 are set equal to p/(N s σ 2 S ) and (1 − p)/(N s σ 2 S ), respectively. The N s and N r are data length for the surface waves and receiver functions respectively.…”

“…To integrate the two sets of data, we minimized the following penalty function (Julià et al, 2000;Maceira and Ammon, 2009):…”

Iterative generalized cross-validation for fusing heteroscedastic data of inverse ill-posed problems

“…Delouis et al 2002;Feigl et al 2002), they have been either assumed known or empirically chosen in joint geophysical inversion (see, e.g. Vozoff & Jupp 1975;Delouis et al 2002;Feigl et al 2002;Gallardo & Meju 2007;Khan et al 2007;Konca et al 2007;Kozlovskaya et al 2007;Amoruso et al 2008;Maceira & Ammon 2009). To the best knowledge of this author, no solid statistical foundation is documented in geophysical literature to let data decide the correct weighting factors of different types of data in joint geophysical inversion of ill-posed problems.…”

“…The stochastic model (5), together with the functional model (1), represents a standard linear ill-posed problem for all joint geophysical inversion (see, e.g. Vozoff & Jupp 1975;Delouis et al 2002;Feigl et al 2002;Kozlovskaya et al 2007;Amoruso et al 2008;Maceira & Ammon 2009). If y = diag(W −1 i σ 2 i ), then (5) simply means that different types of data are combined to jointly solve a geophysical ill-posed problem.…”