Improving the resolution of seismic migration images plays an important role for geophysical interpreters to characterize underground reservoirs. However, the classical image domain least-squares migration method based on the local-stationary assumption cannot obtain a satisfactory high-resolution seismic image due to the significant spatial variant characteristics of the point spread function. To mitigate this problem, we proposed a high-resolution point spread function deconvolution method and applied it to two-dimensional cases. Nevertheless, extending the two-dimensional method to three-dimensional problems directly would fail due to the intrinsic complexity in three-dimensional cases. In this study, we resolve the differences encountered in the point spread function deconvolution method for two- and three-dimensional cases and provide specific strategies for achieving high-resolution imaging with low computational cost when extending the point spread function deconvolution method to three-dimensional cases. The main schemes include (1) incorporating the analytical expression of the point spread function to guide the generation of three-dimensional point spread function distributions, (2) extending the point spread function filter calculation method from a two-dimensional square to a three-dimensional rectangular prism and (3) interpolating to obtain more compact point spread functions for reducing migration artefacts. Results from the three-dimensional synthetic Overthrust model and field data set demonstrate that our techniques could effectively enhance the spatial resolution of the migration images with reduced migration artefacts. With these specific strategies, the space-variant point spread function deconvolution algorithm shows superior performance on three-dimensional cases at a much lower computational cost compared with the classical least-squares migration method and the local-stationary deblurring method. Synthetic tests and real data applications confirm that the space-variant point spread function deconvolution method has distinct advantages over both two- and three-dimensional problems and can be widely adopted in practice.

中文翻译：

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基于空变点扩散函数反卷积提高三维偏移图像分辨率

提高地震偏移图像的分辨率对于地球物理解释人员表征地下储层具有重要作用。然而，由于点扩散函数具有显着的空间变化特征，基于局部平稳假设的经典图像域最小二乘偏移方法无法获得满意的高分辨率地震图像。为了缓解这个问题，我们提出了一种高分辨率点扩散函数反卷积方法并将其应用于二维情况。然而，由于三维情况的内在复杂性，将二维方法直接扩展到三维问题将会失败。在本研究中，我们解决了点扩散函数反卷积方法在二维和三维情况下遇到的差异，并提供了将点扩散函数反卷积方法扩展到三维时以低计算成本实现高分辨率成像的具体策略案例。主要方案包括（1）结合点扩散函数的解析表达式来指导三维点扩散函数分布的生成，（2）将点扩散函数滤波器计算方法从二维正方形扩展到三维。维直角棱柱；(3) 插值以获得更紧凑的点扩散函数，以减少迁移伪影。三维综合逆掩模型和现场数据集的结果表明，我们的技术可以有效地提高偏移图像的空间分辨率，同时减少偏移伪影。通过这些具体策略，与经典的最小二乘偏移方法和局部平稳去模糊方法相比，空变点扩散函数反卷积算法以低得多的计算成本在三维情况下表现出优越的性能。综合测试和实际数据应用证实，空变点扩散函数反卷积方法相对于二维和三维问题具有明显的优势，可以在实际中广泛采用。