We have introduced four approaches that dramatically enhance the application of the inverse-scattering series method for field data internal multiple prediction. The first approach aims to tackle challenges related to input data conditioning and interpolation. We address this through an efficient and fit-for-purpose data regularization strategy, which in this work is a nearest-neighbor search followed by differential moveout to accommodate various acquisition configurations. The second approach addresses cost challenges through application of angle constraints over the dip angle and opening angle, reducing computational cost without compromising model quality. We also develop an automatic solution for parameterization. The third approach segments the prediction by limiting the range of the multiples’ generator, which can benefit the subsequent adaptive subtraction. The fourth approach works on improving predicted model quality. The strategy includes correctly incorporating the 3D source effect and obliquity factor to enhance the amplitude fidelity of the predicted multiples in terms of frequency spectrum and angle information. We illustrate challenges and report on the improvements in cost, quality, or both from the new workflow, using examples from synthetic data and from three field data 2D lines representative of shallow- and deepwater environments.

中文翻译：

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使用逆散射序列增强内部多重预测：方法论和现场应用

我们介绍了四种方法，它们极大地增强了逆散射级数方法在现场数据内部多次预测中的应用。第一种方法旨在解决与输入数据调节和插值相关的挑战。我们通过一种高效且适合目的的数据正则化策略来解决这个问题，在这项工作中，该策略是最近邻搜索，然后是差分时差以适应各种采集配置。第二种方法通过在倾角和张角上应用角度约束来解决成本挑战，在不影响模型质量的情况下降低计算成本。我们还开发了一种用于参数化的自动解决方案。第三种方法通过限制倍数生成器的范围来分割预测，这可以有利于后续的自适应减法。第四种方法致力于提高预测模型的质量。该策略包括正确结合 3D 源效应和倾斜因子，以提高预测多次波在频谱和角度信息方面的幅度保真度。我们使用来自合成数据和代表浅水和深水环境的三个现场数据二维线的示例来说明挑战并报告新工作流程在成本、质量或两者方面的改进。