Ray-parameter based stacking and enhanced pre-conditioning for stable inversion of receiver function data

Abstract

While inversion of seismic velocity from receiver function data could be instable due to\nits intrinsic non-linearity and non-uniqueness, improper stacking of receiver function could\nalso introduce significant biases to the resulting velocity structure. In a distance section of\nreceiver functions, the Moho Ps conversion and the two reverberations possess a positive\nand negative moveout, respectively. Stacking receiver functions without moveout correction\ncould significantly reduce and distort the amplitude and waveform of these phases. Inversion\nwith these incorrectly stacked receiver functions will thus inevitably introduce artefacts to the\nresulting velocity structure. In this study, we have improved the inversion procedure in two\nways. First, we introduce a ray-parameter based (RPB) stacking method to correctly construct\nreceiver function data for inversion. Specifically we develop a ‘four-pin’ method that accounts\nfor the moveout effect of the converted and reverberated phases in stacking individual receiver\nfunctions recorded at various distances. Secondly, we divide the receiver function trace into\nconversion and reverberation windows and assign different weights between the two windows\nin the inversion. More weight is given to the Ps conversion window in resolving the shallow\nstructure, which can be nearly fixed in the successive inversion of deeper structure. We also\nemploy other pre-conditioning proposed by previous studies, such as balancing the receiver\nfunction data being filtered with different Gaussian filters, smoothing the velocity model and\nfurther regulating the model based on existing information. We compute synthetic receiver\nfunctions at distances between 30◦ and 90◦ from a target model and then use the RPB stacking\nmethod to generate the input data for various inversions (iterative linear) with different initial\nmodels. Our inversions with enhanced pre-conditioning and RPB stacked data demonstrate a\ngood capability in recovering the target model from generally more stable iterations. Applying\nthese techniques to two broad-band stations in China indicates that the improvements on data\nstacking and inversion can eliminate potential stacking-induced artefacts, and yield models\nmore consistent with surface geology.