Seismic data reconstruction is an important technology in the seismic data processing. Existing reconstruction methods have achieved promising performance for regularly/randomly missing cases. However, recovering consecutive missing data remains challenging due to the loss of large amounts of information in local regions. In this letter, we devise a novel network called recurrent residual multiscale feature inference network (RRMFI-Net), which is mainly constructed by a recurrent residual multiscale feature inference (RRMFI) module and a recurrence adjustment attention (RAA) module. The RRMFI module infers and fills the missing regions multiple times and uses the result as a clue for the next inference, which makes the result more elegant. To ensure that there is no ambiguity between the results of multiple inferences, we devise an RRA module, which is fused into the RRMFI module to obtain padding information from a long distance. Experimentally, we compare RRMFI-Net with supervised state-of-the-art methods, demonstrating that RRMFI-Net is more effective on multiple indicators. Furthermore, we conduct ablation studies discussing the impact of key network hyperparameters.

中文翻译：

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通过递归残差多尺度推理重建地震数据

地震资料重建是地震资料处理中的一项重要技术。现有的重建方法在定期/随机缺失的情况下取得了可喜的表现。然而，由于局部区域的大量信息丢失，恢复连续丢失的数据仍然具有挑战性。在这封信中，我们设计了一种称为循环残差多尺度特征推理网络（RRMFI-Net）的新型网络，该网络主要由循环残差多尺度特征推理（RRMFI）模块和递归调整注意力（RAA）模块构成。RRMFI 模块多次推断和填充缺失的区域，并将结果作为下次推断的线索，这使得结果更加优雅。为了确保多次推理的结果之间没有歧义，我们设计了一个 RRA 模块，将其融合到 RRMFI 模块中，以从远距离获取填充信息。在实验上，我们将 RRMFI-Net 与有监督的最先进方法进行比较，证明 RRMFI-Net 在多个指标上更有效。此外，我们进行消融研究，讨论关键网络超参数的影响。