采用定制化U-Net衰减海洋地震干扰噪声
Attenuation of marine seismic interference noise employing a customized U-Net
AI总结：
针对海洋地震干扰噪声传统去噪方法耗时、自编码器效果差的问题，提出带逐元素求和跳跃连接的定制U-Net，用25000组现场数据训练，速度远超商业算法，且支持单炮顺序处理。
摘要：
海洋地震干扰噪声是在地震勘探过程中，附近海洋地震震源船产生的能量被记录下来时出现的噪声。这类噪声往往能在远距离上良好保持，并在记录数据中造成相干伪影。多年来，行业已开发出多种用于去除地震干扰的去噪技术，但这些技术尽管效果不错，使用时仍十分耗时。基于机器学习的处理是一种替代方法，可显著提升计算效率。对于常规图像，自编码器常被用于去噪任务。然而，由于地震数据及噪声的特殊特性，自编码器在处理海洋地震干扰噪声时效果不佳。因此，我们提出采用一种定制化U-Net设计，在跳跃连接模块中加入逐元素求和操作，以解决梯度消失问题并确保高低层特征之间的信息融合。为保证研究的真实性，我们仅使用了地震现场数据，其中包含25000个训练样本。研究发现，该定制化U-Net表现良好，仅会残留极小的噪声残余，仅在地震干扰噪声来自侧面的情况下效果稍差。我们进一步证明，只需略微增加网络深度即可处理这类噪声。尽管我们的定制化U-Net在去噪质量上并未超越标准商业算法，但它（经过适当训练后）读取并处理单个炮集仅需约0.02秒，这一速度远快于现有任何行业去噪算法。此外，所提出的网络按顺序处理炮集，相较于通常需要多炮输入来打破噪声相干性的行业算法而言，这是一项优势。
英文摘要：
Marine seismic interference noise occurs when energy from nearby marine seismic source vessels is recorded during a seismic survey. Such noise tends to be well preserved over large distances and cause coherent artifacts in the recorded data. Over the years, the industry has developed various denoising techniques for seismic interference removal, but although well performing they are still time-consuming in use. Machine-learning based processing represents an alternative approach, which may significantly improve the computational efficiency. In case of conventional images, autoencoders are frequently employed for denoising purposes. However, due to the special characteristics of seismic data as well as the noise, autoencoders failed in the case of marine seismic interference noise. We therefore propose the use of a customized U-Net design with element-wise summation as part of the skip-connection blocks to handle the vanishing gradient problem and to ensure information fusion between high- and low-level features. To secure a realistic study, only seismic field data were employed, including 25000 training examples. The customized U-Net was found to perform well leaving only minor residuals, except for the case when seismic interference noise comes from the side. We further demonstrate that such noise can be treated by slightly increasing the depth of our network. Although our customized U-Net does not outperform a standard commercial algorithm in quality, it can (after proper training) read and process one single shot gather in approximately 0.02s. This is significantly faster than any existing industry denoising algorithm. In addition, the proposed network processes shot gathers in a sequential order, which is an advantage compared with industry algorithms that typically require a multi-shot input to break the coherency of the noise.
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