Abstract

SUMMARY Dynamic ray tracing is a robust and efficient method for computation of amplitude and phase attributes of the high-frequency Green’s function. A formulation of dynamic ray tracing in Cartesian coordinates was recently extended to higher orders. Extrapolation of traveltime and geometrical spreading was demonstrated to yield significantly higher accuracy—for isotropic as well as anisotropic heterogeneous 3-D models of an elastic medium. This is of value in mapping, modelling and imaging, where kernel operations are based on extrapolation or interpolation of Green’s function attributes to densely sampled 3-D grids. We introduce higher-order dynamic ray tracing in ray-centred coordinates, which has certain advantages: (1) such coordinates fit naturally with wave propagation; (2) they lead to a reduction of the number of ordinary differential equations; (3) the initial conditions are simple and intuitive and (4) numerical errors due to redundancies are less likely to influence the computation of the Green’s function attributes. In a 3-D numerical example, we demonstrate that paraxial extrapolation based on higher-order dynamic ray tracing in ray-centred coordinates yields results highly consistent with those obtained using Cartesian coordinates. Furthermore, in a 2-D example we show that interpolation of dynamic ray tracing quantities along a wavefront can be done with much better consistency in ray-centred coordinates than in Cartesian coordinates. In both examples we measure consistency by means of constraints on the dynamic ray tracing quantities in the 3-D position space and in the 6-D phase space.

Highlights

  • IntroductionDynamic ray tracing is established in seismology and seismic exploration as a robust and efficient method for computation of amplitude and phase attributes of the high-frequency Green’s function (e.g. Cerveny 1972, 2001; Cervenyet al. 1977, 1984, 2012; Popov & Psenc ́ık 1978; Cerveny & Hron 1980; Hanyga 1982; Farra & Madariaga 1987; Gajewski & Psenc ́ık 1990; Kendall et al 1992; Klimes 1994; Chapman 2004; Iversen 2004a; Klimes 2006b; Cerveny & Moser 2007; Cerveny & Psenc ́ık 2010).The accuracy of the Green’s function attributes, in particular regarding traveltime, is key in the construction of an initial velocity model for full-waveform inversion (FWI)

  • In a numerical examples section, we show that paraxial extrapolation based on higher-order dynamic ray tracing in ray-centred coordinates yields results highly consistent with those obtained previously using Cartesian coordinates

  • The methodology applies to 3-D anisotropic heterogeneous models

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Summary

Introduction

Dynamic ray tracing is established in seismology and seismic exploration as a robust and efficient method for computation of amplitude and phase attributes of the high-frequency Green’s function (e.g. Cerveny 1972, 2001; Cervenyet al. 1977, 1984, 2012; Popov & Psenc ́ık 1978; Cerveny & Hron 1980; Hanyga 1982; Farra & Madariaga 1987; Gajewski & Psenc ́ık 1990; Kendall et al 1992; Klimes 1994; Chapman 2004; Iversen 2004a; Klimes 2006b; Cerveny & Moser 2007; Cerveny & Psenc ́ık 2010).The accuracy of the Green’s function attributes, in particular regarding traveltime, is key in the construction of an initial velocity model for full-waveform inversion (FWI). As shown recently by Djebbi & Alkhalifah (2020), sensitivity kernels computed by dynamic ray tracing can be used in the FWI model update—this is beneficial with respect to the computation speed and the computer memory required to store the Green’s function attributes. Another example on the use of ray theory for FWI is Zhou et al (2018), who compute two-way reflection wave paths to update the velocity model. The wavefront construction method (e.g. Vinje et al 1993; Lambareet al. 1996; Gibson et al 2005), which is based on dynamic ray tracing, omits this limitation and provides

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