S2S-WTV：利用加权全变分正则化自监督学习进行地震数据噪声衰减
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning
AI总结：
本文提出 S2S-WTV，在 Self2Self 自监督框架中引入按道掩码与加权全变分正则化，并用 ADMM 求解，仅依赖含噪数据即可稳健去噪并保持信号边缘。
摘要：
地震数据常因环境因素而遭受严重噪声，这会严重影响后续应用。滤波器和正则化等传统手工去噪器利用可解释的领域知识来设计具有泛化能力的去噪技术，但其表示能力可能不如深度学习去噪器，后者能够从大量训练对中学习复杂且具代表性的去噪映射。然而，由于高质量训练对稀缺，深度学习去噪器在各种场景下可能存在一定的泛化问题。在本工作中，我们提出一种自监督方法，将深度去噪器的能力与手工正则化的泛化能力相结合，用于地震数据随机噪声衰减。具体而言，我们利用 Self2Self（S2S）学习框架，并采用按道掩码策略，仅使用观测到的含噪数据进行地震数据去噪。同时，我们提出加权全变分（WTV），以进一步捕捉地震数据的水平局部平滑结构。我们的方法被命名为 S2S-WTV，兼具自监督深度网络带来的高表示能力，以及手工 WTV 正则化项和自监督本质所具有的良好泛化能力。因此，我们的方法能够更有效、更稳定地去除随机噪声，并保留干净信号的细节和边缘。为求解 S2S-WTV 优化模型，我们引入一种基于交替方向乘子法（ADMM）的算法。在合成和野外含噪地震数据上的大量实验表明，与最先进的传统方法及基于深度学习的地震数据去噪方法相比，我们的方法具有有效性。
英文摘要：
Seismic data often undergoes severe noise due to environmental factors, which seriously affects subsequent applications. Traditional hand-crafted denoisers such as filters and regularizations utilize interpretable domain knowledge to design generalizable denoising techniques, while their representation capacities may be inferior to deep learning denoisers, which can learn complex and representative denoising mappings from abundant training pairs. However, due to the scarcity of high-quality training pairs, deep learning denoisers may sustain some generalization issues over various scenarios. In this work, we propose a self-supervised method that combines the capacities of deep denoiser and the generalization abilities of hand-crafted regularization for seismic data random noise attenuation. Specifically, we leverage the Self2Self (S2S) learning framework with a trace-wise masking strategy for seismic data denoising by solely using the observed noisy data. Parallelly, we suggest the weighted total variation (WTV) to further capture the horizontal local smooth structure of seismic data. Our method, dubbed as S2S-WTV, enjoys both high representation abilities brought from the self-supervised deep network and good generalization abilities of the hand-crafted WTV regularizer and the self-supervised nature. Therefore, our method can more effectively and stably remove the random noise and preserve the details and edges of the clean signal. To tackle the S2S-WTV optimization model, we introduce an alternating direction multiplier method (ADMM)-based algorithm. Extensive experiments on synthetic and field noisy seismic data demonstrate the effectiveness of our method as compared with state-of-the-art traditional and deep learning-based seismic data denoising methods.
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