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Volume 67, Issue 7
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

A fast and robust method for two‐point ray tracing in one‐dimensional layered media is presented. This method is applicable to layered models with constant or linearly varying isotropic layer velocity. For given model properties and source and receiver positions, a ray path can be uniquely determined once its ray parameter (i.e. horizontal slowness) is known. The ray parameter can be obtained by numerically solving the nonlinear offset (i.e. source–receiver horizontal distance) equation using Newton's method, which generally works well at near and mid offsets. However, Newton's method becomes hard to converge at large offsets due to the oversensitivity of offset to ray parameter. Based on the analysis of the characteristic of the offset equation, a modified ray parameter is proposed and used to replace the generic ray parameter in numerical calculation. Numerical experiments show that the iteration process becomes stable and converges rapidly with the modified ray parameter. Moreover, a rational function that asymptotically approximates the shape of the offset equation is introduced for obtaining good initial estimates of the modified ray parameter. Numerical tests show that this method is robust in any situation, and an accurate ray parameter can be obtained within two or three iterations for a wide range of model velocity structure and source–receiver distance. Furthermore, the proposed two‐point ray tracing method is easy to implement.

© 2019 European Association of Geoscientists & Engineers © 2019 European Association of Geoscientists & Engineers
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/content/journals/10.1111/1365-2478.12799
2019-05-09
2024-04-20

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A fast and robust two‐point ray tracing method in layered media with constant or linearly varying layer velocity
Geophysical Prospecting 67, 1811 (2019); https://doi.org/10.1111/1365-2478.12799
/content/journals/10.1111/1365-2478.12799
/content/journals/10.1111/1365-2478.12799
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  • Article Type: Research Article
Keyword(s): Modelling; Rays; Seismics

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