ABSTRACT Full-waveform inversion (FWI) is one of the most promising inversion methods in geophysics due to its theoretical completeness and high resolution. However, the inversion ability of FWI strongly relies on the accuracy of the initial model and the quality of the low-frequency data. For FWI, it is important to accurately recover low and middle wavenumber components (the background model). In order to invert for the background model, we use a new strategy to compose the plane-wave using reference source points. With the new algorithm, more plane-waves can be composed for a single ray parameter to obtain the background model by plane-wave multi-scale full-waveform inversion (PMFWI) method. By controlling the ray parameter in this method, the low to middle wavenumber components can be recovered in sequence. Composing several plane-waves for a single ray parameter enables the illumination to be significantly improved, even for incomplete data. As only a small number of plane-waves are needed in this method, the computation burden greatly decreases. Analysis of numerical tests also verifies that the proposed inversion strategy is robust, to a certain extent, for high-frequency or noisy data. Application of this method on a modified portion of a SigsBee 2A model illustrates that, combined with conventional FWI, PMFWI has suitable model accuracy, even for coarse initial model data and high-frequency data.

中文翻译：

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平面波多源多尺度全波形反演直接构建背景速度场

摘要 全波形反演（FWI）由于其理论完备性和高分辨率，是地球物理学中最有前途的反演方法之一。然而，FWI 的反演能力强烈依赖于初始模型的准确性和低频数据的质量。对于 FWI，准确恢复中低波数分量（背景模型）非常重要。为了对背景模型进行反演，我们使用了一种新的策略来使用参考源点合成平面波。新算法可以将单个射线参数组合成更多的平面波，通过平面波多尺度全波形反演(PMFWI)方法得到背景模型。通过控制该方法中的射线参数，可以依次恢复中低波数分量。为单个光线参数组合多个平面波可以显着改善照明，即使对于不完整的数据也是如此。由于该方法只需要少量的平面波，计算负担大大降低。数值测试的分析也验证了所提出的反演策略在一定程度上对于高频或噪声数据是稳健的。将该方法应用于 SigsBee 2A 模型的修改部分表明，与常规 FWI 相结合，PMFWI 具有合适的模型精度，即使对于粗糙的初始模型数据和高频数据也是如此。数值测试的分析也验证了所提出的反演策略在一定程度上对于高频或噪声数据是稳健的。将该方法应用于 SigsBee 2A 模型的修改部分表明，与常规 FWI 相结合，PMFWI 具有合适的模型精度，即使对于粗糙的初始模型数据和高频数据也是如此。数值测试的分析也验证了所提出的反演策略在一定程度上对于高频或噪声数据是稳健的。将该方法应用于 SigsBee 2A 模型的修改部分表明，与常规 FWI 相结合，PMFWI 具有合适的模型精度，即使对于粗糙的初始模型数据和高频数据也是如此。