Browsing by Author "Chen, Youlin"

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    Joint inversion of receiver functions and surface waves with enhanced preconditioning on densely distributed CNDSN stations: Crustal and upper mantle structure beneath China
    (Wiley, 2016) Chen, Youlin; Niu, Fenglin
    We present shear wave velocity structure beneath China by joint modeling of teleseismic receiver function and Rayleigh wave group velocity dispersion data observed at +1000 permanent broadband seismic stations in the Chinese National Digital Seismic Network (CNDSN). A ray-parameter-based stacking method is employed to minimize artifacts in stacking receiver functions from different sources. The Rayleigh wave dispersion curve is extracted from group velocity tomographic models at all applicable periods. Enhanced preconditions are applied on the linearized iterative inversion to regularize and balance multiple types of data. The velocity profile inversion at each station starts from an initial model derived from sediments, crustal thickness, Vp/Vs ratio and Pn/Sn models. This multistep approach not only reduces uncertainty and nonuniqueness of the velocity inversion but also efficiently fills information gap in each data set. We then generate a 3-D S velocity model by combining and smoothing all the 1-D models. The obtained 3-D model reveals crustal and upper mantle velocity structures that are well correlated with tectonic features of China, for example, our model shows a clear east-west bimodal distribution at 35 km deep, low velocity in the crust beneath central and eastern Tibetan plateau, and sedimentary structure in major cratons and basins. Our model is consistent with existing tomographic models in large scale but provides more structural details in regional and local scales.
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    Ray-parameter based stacking and enhanced pre-conditioning for stable inversion of receiver function data
    (Oxford University Press on behalf of The Royal Astronomical Society, 2013) Chen, Youlin; Niu, Fenglin
    While inversion of seismic velocity from receiver function data could be instable due to its intrinsic non-linearity and non-uniqueness, improper stacking of receiver function could also introduce significant biases to the resulting velocity structure. In a distance section of receiver functions, the Moho Ps conversion and the two reverberations possess a positive and negative moveout, respectively. Stacking receiver functions without moveout correction could significantly reduce and distort the amplitude and waveform of these phases. Inversion with these incorrectly stacked receiver functions will thus inevitably introduce artefacts to the resulting velocity structure. In this study, we have improved the inversion procedure in two ways. First, we introduce a ray-parameter based (RPB) stacking method to correctly construct receiver function data for inversion. Specifically we develop a ‘four-pin’ method that accounts for the moveout effect of the converted and reverberated phases in stacking individual receiver functions recorded at various distances. Secondly, we divide the receiver function trace into conversion and reverberation windows and assign different weights between the two windows in the inversion. More weight is given to the Ps conversion window in resolving the shallow structure, which can be nearly fixed in the successive inversion of deeper structure. We also employ other pre-conditioning proposed by previous studies, such as balancing the receiver function data being filtered with different Gaussian filters, smoothing the velocity model and further regulating the model based on existing information. We compute synthetic receiver functions at distances between 30◦ and 90◦ from a target model and then use the RPB stacking method to generate the input data for various inversions (iterative linear) with different initial models. Our inversions with enhanced pre-conditioning and RPB stacked data demonstrate a good capability in recovering the target model from generally more stable iterations. Applying these techniques to two broad-band stations in China indicates that the improvements on data stacking and inversion can eliminate potential stacking-induced artefacts, and yield models more consistent with surface geology.