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Time-domain incomplete Gauss-Newton full-waveform inversion of Gulf of Mexico data

机译:墨西哥湾数据的时域不完整高斯-牛顿全波形反演

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摘要

We apply the incomplete Gauss-Newton full-waveform inversion (TDIGN-FWI) to Gulf of Mexico (GOM) data in the space-time domain. In our application, iterative least-squares reverse-time migration (LSRTM) is used to estimate the model update at each non-linear iteration, and the number of LSRTM iterations is progressively increased after each non-linear iteration. With this method, model updating along deep reflection wavepaths are automatically enhanced, which in turn improves imaging below the reach of diving-waves. The forward and adjoint operators are implemented in the space-time domain to simultaneously invert the data over a range of frequencies. A multiscale approach is used where higher frequencies are down-weighted significantly at early iterations, and gradually included in the inversion.ududSynthetic data results demonstrate the effectiveness of reconstructing both the high- and low-wavenumber features in the model without relying on diving waves in the inversion. Results with Gulf of Mexico field data show a significantly improved migration image in both the shallow and deep sections.
机译:我们将不完整的高斯-牛顿全波形反演(TDIGN-FWI)应用于时空域的墨西哥湾(GOM)数据。在我们的应用中,迭代最小二乘逆时偏移(LSRTM)用于估计每个非线性迭代的模型更新,并且每个非线性迭代后LSRTM迭代的数量逐渐增加。通过这种方法,将自动增强沿深反射波路径的模型更新,从而在潜水波范围内改善成像效果。在时空域中实现前向运算符和伴随运算符,以同时在一定频率范围内反转数据。使用多尺度方法,其中较高的频率在早期迭代中会明显降低权重,并逐渐包含在反演中。 ud ud综合数据结果证明了在不依赖模型的情况下重构模型中的高波数和低波数特征的有效性跳水在反转中。墨西哥湾实地数据的结果表明,浅层和深层的迁移图像均得到了显着改善。

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