首页> 外文期刊>Inverse Problems: An International Journal of Inverse Problems, Inverse Methods and Computerised Inversion of Data >One dimensional acoustic direct nonlinear inversion using the Volterra inverse scattering series
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One dimensional acoustic direct nonlinear inversion using the Volterra inverse scattering series

机译:利用Volterra逆散射级数进行一维声波直接非线性反演。

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摘要

Direct inversion of acoustic scattering problems is nonlinear. One way to treat the inverse scattering problem is based on the reversion of the Born–Neumann series solution of the Lippmann–Schwinger equation. An important issue for this approach is the radius of convergence of the Born–Neumann series for the forward problem. However, this issue can be tackled by employing a renormalization technique to transform the Lippmann–Schwinger equation from a Fredholm to a Volterra integral form. The Born series of a Volterra integral equation converges absolutely and uniformly in the entire complex plane. We present a further study of this new mathematical framework. A Volterra inverse scattering series (VISS) using both reflection and transmission data is derived and tested for several acoustic velocity models. For large velocity contrast, series summation techniques (e.g., Cesàro summation, Euler transform, etc) are employed to improve the rate of convergence of VISS. It is shown that the VISS method with summation techniques can provide a relatively good estimation of the velocity profile. The method is fully data-driven in the respect that no prior information of the model is required. Besides, no internal multiple removal is needed. This one dimensional VISS approach is useful for inverse scattering and serves as an important step for studying more complicated and realistic inversions.
机译:声散射问题的直接反演是非线性的。解决逆散射问题的一种方法是基于Lippmann-Schwinger方程的Born-Neumann级数解的逆转。该方法的一个重要问题是前向问题的Born–Neumann级数的收敛半径。但是,可以通过使用重新规范化技术将Lippmann-Schwinger方程从Fredholm转换为Volterra积分形式来解决。 Volterra积分方程的Born级数在整个复平面上绝对且一致地收敛。我们对这个新的数学框架进行了进一步的研究。导出了同时使用反射和透射数据的Volterra反散射序列(VISS)并针对几种声速模型进行了测试。对于大的速度对比,采用串联求和技术(例如Cesàro求和,欧拉变换等)来提高VISS的收敛速度。结果表明,具有求和技术的VISS方法可以提供相对较好的速度分布估计。在不需要模型的先验信息方面,该方法完全由数据驱动。此外,不需要内部多次清除。这种一维VISS方法对于逆散射很有用,并且是研究更复杂和现实的反演的重要步骤。

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