Blind sparse-spike deconvolution is a widely used method to estimate seismic wavelets and sparse reflectivity in the shape of spikes based on the convolution model. To increase the vertical resolution and lateral continuity of the estimated reflectivity, we further improve the sparse-spike deconvolution by introducing the atomic norm minimization and structural regularization, respectively. Specifically, we use the atomic norm minimization to estimate the reflector locations, which are further used as position constraints in the sparse-spike deconvolution. By doing this, we can vertically separate highly thin layers through the sparse deconvolution. In addition, the seismic structural orientations are estimated from the seismic image to construct a structure-guided regularization in the deconvolution to preserve the lateral continuity of reflectivities. Our improvements are suitable for most types of sparse-spike deconvolution approaches. The sparse-spike deconvolution method with Toeplitz-sparse matrix factorization (TSMF) is used as an example to demonstrate the effectiveness of our improvements. Synthetic and real examples show that our methods perform better than TSMF in estimating the reflectivity of thin layers and preserving the lateral continuities.

中文翻译：

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薄层和结构的盲稀疏峰值反卷积

盲稀疏峰反卷积是一种基于卷积模型来估计地震子波和峰状的稀疏反射率的广泛使用的方法。为了增加估计的反射率的垂直分辨率和横向连续性，我们分别通过引入原子范数最小化和结构规则化来进一步改善稀疏峰反褶积。具体来说，我们使用原子范数最小化来估计反射器的位置，这些位置在稀疏峰值反卷积中进一步用作位置约束。通过这样做，我们可以通过稀疏反卷积垂直分离高度薄的层。此外，从地震图像估计地震结构的方向，以在反卷积中构造结构指导的正则化，以保持反射率的横向连续性。我们的改进适用于大多数类型的稀疏峰值反卷积方法。以具有Toeplitz-稀疏矩阵分解（TSMF）的稀疏峰值反卷积方法为例来说明我们的改进的有效性。综合和实际的例子表明，我们的方法在估计薄层的反射率和保留横向连续性方面比TSMF更好。以具有Toeplitz-稀疏矩阵分解（TSMF）的稀疏峰值反卷积方法为例来说明我们的改进的有效性。综合和真实的例子表明，我们的方法在估计薄层的反射率和保持横向连续性方面比TSMF更好。以具有Toeplitz-稀疏矩阵分解（TSMF）的稀疏峰值反卷积方法为例来说明我们的改进的有效性。综合和实际的例子表明，我们的方法在估计薄层的反射率和保留横向连续性方面比TSMF更好。