ABSTRACT In this paper, we compare the denoising‐ and inversion‐based deblending methods using Stolt migration operators. We use Stolt operator as a kernel to efficiently compute apex‐shifted hyperbolic Radon transform. Sparsity promoting transforms, such as Radon transform, can focus seismic data into a sparse model to separate signals, remove noise or interpolate missing traces. Therefore, Radon transforms are a suitable tool for either the denoising‐ or the inversion‐based deblending methods. The denoising‐based deblending treats blending interferences as random noise by sorting the data into new gathers, such as common receiver gather. In these gathers, blending interferences exhibit random structures due to the randomization of the source firing times. Alternatively, the inversion‐based deblending treats blending interferences as a signal, and the transform models this signal by incorporating the blending operator to formulate an inversion problem. We compare both methods using a robust inversion algorithm with sparse regularization. Results of synthetic and field data examples show that the inversion‐based deblending can produce more accurate signal separation for highly blended data.

中文翻译：

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使用迁移运算符进行基于反演的去混合

摘要 在本文中，我们使用 Stolt 迁移算子比较了基于去噪和基于反演的去混合方法。我们使用 Stolt 算子作为内核来有效地计算顶点位移双曲 Radon 变换。稀疏促进变换，例如 Radon 变换，可以将地震数据集中到稀疏模型中，以分离信号、去除噪声或插入缺失的轨迹。因此，Radon 变换是基于去噪或基于反演的去混合方法的合适工具。基于去噪的去混合通过将数据分类到新的集合（例如公共接收器集合）中将混合干扰视为随机噪声。在这些道集中，由于源发射时间的随机化，混合干扰表现出随机结构。或者，基于反演的去混合将混合干扰视为一个信号，并且变换通过结合混合算子来对这个信号进行建模以制定反演问题。我们使用具有稀疏正则化的稳健反演算法来比较这两种方法。合成和现场数据示例的结果表明，基于反演的去混合可以为高度混合的数据产生更准确的信号分离。