The presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self-supervised denoising circumvents the common requirement of deep learning procedures of having noisy-clean training pairs. However, self-supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to ‘see inside the black box’ that is the denoising network and use the gained knowledge to replace the need for any prior knowledge of the noise itself. This is achieved in practice by leveraging bias-free networks and the direct linear link between input and output provided by the associated Jacobian matrix; we show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels provides an indication of the most effective mask to suppress noise present in the data. The proposed method, therefore, becomes a fully automated denoising procedure requiring no clean training labels or prior knowledge. Realistic synthetic examples with noise signals of varying complexities, ranging from simple time-correlated noise to complex pseudo-rig noise propagating at the velocity of the ocean, are used to validate the proposed approach. Its automated nature is highlighted further by an application to two field data sets. Without any substantial pre-processing or any knowledge of the acquisition environment, the automatically identified blind masks are shown to perform well in suppressing both trace-wise noise in common shot gathers from the Volve marine data set and coloured noise in post-stack seismic images from a land seismic survey.

中文翻译：

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可解释的人工智能驱动的掩模设计，用于自监督地震降噪

地震数据中相干噪声的存在会导致误差和不确定性，因此尽早有效地抑制噪声至关重要。自监督去噪规避了深度学习过程中具有噪声-干净训练对的常见要求。然而，自监督相干噪声抑制方法需要广泛的噪声统计知识。我们建议使用可解释的人工智能方法来“查看黑匣子内部”（即去噪网络），并使用获得的知识来取代对噪声本身的任何先验知识的需要。在实践中，这是通过利用无偏差网络以及相关雅可比矩阵提供的输入和输出之间的直接线性链接来实现的；我们证明，对多个随机选择的输入像素的雅可比贡献的简单平均可以提供抑制数据中存在的噪声的最有效掩模的指示。因此，所提出的方法成为一种完全自动化的去噪程序，不需要干净的训练标签或先验知识。使用具有不同复杂性的噪声信号的现实合成示例来验证所提出的方法，从简单的时间相关噪声到以海洋速度传播的复杂伪钻机噪声。通过对两个现场数据集的应用进一步强调了其自动化特性。在没有任何实质性的预处理或对采集环境的任何了解的情况下，自动识别的盲掩模在抑制来自沃尔沃海洋数据集的共炮道集中的轨迹噪声和叠后地震图像中的有色噪声方面表现良好来自陆地地震勘测。