Effectively suppressing seismic multiples relies heavily on the crucial task of adaptively subtracting the simulated multiples from the initial recorded data. By executing adaptive subtraction within the nonlinear regression (non-LR) framework, the U-net method (UNetM) has shown superior capability in mitigating the intricate disparities between the simulated and actual multiples when compared with the LR method. The low-, medium-, and high-frequency bands of simulated multiples have been used to effectively address frequency-dependent inconsistencies in the LR method. To further improve multiple suppression accuracy, three frequency bands (TFBs) of simulated multiples are used as three channels of the U-net input, which are matched with the initial recorded data during self-supervised training in this letter. Compared with the LR method inputting simulated multiples alone, the LR method inputting TFBs of simulated multiples, and the UNetM inputting simulated multiples alone, the proposed UNetM inputting TFBs of simulated multiples improves the signal-to-noise ratio (SNR) by 4.41, 2.07, and 1.99 in the synthetic data example and demonstrates superior improvement in preserving primaries and eliminating residual multiples in the field data example.

中文翻译：

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基于 U-Net 的自适应减法，使用模拟多次波的三个频段进行抑制

有效抑制地震多次波在很大程度上依赖于从初始记录数据中自适应减去模拟多次波的关键任务。通过在非线性回归（非 LR）框架内执行自适应减法，与 LR 方法相比，U-net 方法 (UNetM) 在缓解模拟倍数与实际倍数之间复杂的差异方面表现出了卓越的能力。模拟多次波的低、中、高频段已被用来有效解决 LR 方法中与频率相关的不一致问题。为了进一步提高多次波抑制精度，本文采用模拟多次波的三个频段（TFB）作为U-net输入的三个通道，与自监督训练时的初始记录数据进行匹配。与单独输入模拟多次波的LR方法、输入模拟多次波TFB的LR方法以及单独输入模拟多次波的UNetM方法相比，所提出的输入模拟多次波TFB的UNetM方法的信噪比（SNR）提高了4.41、2.07。 ，在合成数据示例中为 1.99，并在现场数据示例中展示了在保留原色和消除残留倍数方面的卓越改进。