Existing deep learning-based seismic data denoising methods mainly involve supervised learning, in which a denoising network is trained using a large amount of noisy input/clean label pairs. However, the scarcity of high-quality clean labels in practice limits the applicability of these methods. Recently, the blind spot (BS) strategy in the field of image processing has attracted extensive attention. Under the assumption that the noise is statistically independent and the true signal exhibits some correlation, the BS strategy allows us to estimate a denoiser from the noisy data itself. In this article, we study the application of the BS strategy to the random noise attenuation of seismic data and propose an unsupervised blind spot network (BSnet) method. Specifically, considering the characteristics of the random noise, we improve the commonly used Unet network and design two types of randomly mask operators to deal with Gaussian white noise and bandpass noise. Synthetic and real data experiments validate the effectiveness of the proposed method.

中文翻译：

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BSnet：用于地震数据随机噪声衰减的无监督盲点网络

现有的基于深度学习的地震数据去噪方法主要涉及监督学习，其中使用大量噪声输入/干净标签对训练去噪网络。然而，实践中高质量清洁标签的稀缺限制了这些方法的适用性。近来，图像处理领域的盲点（BS）策略引起了广泛关注。在噪声在统计上独立且真实信号表现出某种相关性的假设下，BS 策略允许我们从噪声数据本身估计去噪器。在本文中，我们研究了BS策略在地震数据随机噪声衰减中的应用，并提出了一种无监督盲点网络（BSnet）方法。具体来说，考虑到随机噪声的特性，我们改进了常用的Unet网络，设计了两种随机掩码算子来处理高斯白噪声和带通噪声。合成和真实数据实验验证了所提出方法的有效性。