Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending of Extremely Noisy Simultaneous-Source Data
Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending of Extremely Noisy Simultaneous-Source Data
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DOI：
10.1109/tgrs.2018.2872416
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发表时间：
2019-04
影响因子：
8.2
通讯作者：
S. Zu;Hui Zhou;R. Wu;W. Mao;Yangkang Chen
中科院分区：
文献类型：
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作者：
S. Zu;Hui Zhou;R. Wu;W. Mao;Yangkang Chen
同源采集突破了常规地震采集的局限性，以其在缩短勘探时间、提高资料质量等方面的优势，成为一个迅速发展的研究领域。由于强烈的混合干扰，有害源采集的好处受到损害。将一个混合记录分离成一组单独的记录，称为“去混合”，是解决这个问题的最流行的方法之一。然而，混合记录往往被随机噪声污染，这给分离带来困难。在迭代去混合算法中，可以模拟非相干干扰并从混合记录中减去。当随机噪声较强时，非相干干扰的模拟比较困难。在本文中，我们提出了一种混合稀疏约束模型，该模型将字典学习应用到基于稀疏促进变换的去混合框架中，以处理具有极强噪声的同时源数据。基于自适应的字典学习方法可以将非相干干涉学习到原子中，并能有效地抑制随机噪声。然后，基于稀疏变换的框架实现迭代分离的信号和干扰。我们使用两个合成的例子来证明所提出的方法在非常嘈杂的情况下的优势。两个现场实例进一步证实了该方法的上级去混性能优于基于曲波变换和基于降秩的方法。
Simultaneous-source acquisition, breaking the limit of conventional seismic acquisition, is a rapidly evolving research field, due to its advantage in reducing survey time and improving data quality. The benefits of simultaneous-source acquisition are compromised by the intense blending interference. Separating a blended record into a group of individual records, known as “deblending” is one of the most popular solution to the problem. However, the blended records are often corrupted by random noise, which causes difficulties in separation. In an iterative deblending algorithm, the incoherent interference can be simulated and subtracted from the blended record. When the random noise is strong, it is difficult to simulate the incoherent interference. In this paper, we propose a hybrid-sparsity constraint model that applies the dictionary learning into the deblending framework that is based on the sparsity-promoting transform to deal with extremely noisy simultaneous source data. The dictionary learning with fine-tuned adaptation can learn the incoherent interference into atoms and reject random noise. Then, the sparse transform-based framework is implemented to iteratively separate the signal and interference. We use two synthetic examples to demonstrate the advantage of the proposed method in extremely noisy situations. Two field examples further confirm the superior deblending performance of the proposed method for the noisy simultaneous-source data over the curvelet transform-based and rank reduction-based methods.