Least-squares migration (LSM) is an effective technique for mitigating blurring effects and migration artifacts generated by limited data frequency bandwidth, incomplete coverage of geometry, source signature, and unbalanced amplitudes caused by complex wavefield propagation in the subsurface. Migration deconvolution (MD) is an image-domain approach for LSM that approximates the Hessian operator using a set of precomputed point spread functions. We have developed a new workflow by integrating the MD and domain decomposition (DD) methods. DD techniques aim to solve large and complex linear systems by splitting problems into smaller parts, facilitating parallel computing, and providing a higher convergence in iterative algorithms. We suggest that instead of solving the problem in a unique domain, as conventionally performed, splitting the problem into subdomains that overlap and solve each of them independently. We accelerate the convergence rate of the conjugate-gradient solver by applying the DD methods to retrieve better reflectivity, which is mainly visible in regions with low amplitudes. Moreover, using the pseudo-Hessian operator, the convergence of the algorithm is accelerated, suggesting that the inverse problem becomes better conditioned. Experiments using the synthetic Pluto model demonstrate that our algorithm dramatically reduces the required number of iterations while providing a considerable enhancement in image resolution and better continuity of poorly illuminated events.

中文翻译：

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使用域分解的迁移反卷积

最小二乘迁移（LSM）是一种有效的技术，可减轻由于有限的数据频率带宽，几何图形的不完全覆盖，震源特征以及地下复杂波场传播引起的振幅不平衡所产生的模糊效应和迁移伪影。迁移反卷积（MD）是LSM的一种图像域方法，它使用一组预先计算的点扩展函数来近似Hessian算子。我们通过集成MD和域分解（DD）方法开发了一种新的工作流程。DD技术旨在通过将问题分解为较小的部分，促进并行计算并在迭代算法中提供更高的收敛性来解决大型复杂的线性系统。我们建议，不要像通常那样在一个独特的领域中解决问题，将问题分为多个子域，每个子域重叠并独立解决每个子域。我们通过应用DD方法检索更好的反射率来加快共轭梯度求解器的收敛速度，该反射率主要在低振幅区域中可见。此外，使用伪Hessian算子可以加快算法的收敛速度，这表明反问题的条件更好。使用合成冥王星模型进行的实验表明，我们的算法大大减少了所需的迭代次数，同时显着提高了图像分辨率，并改善了不良光照事件的连续性。我们通过应用DD方法检索更好的反射率来加快共轭梯度求解器的收敛速度，该反射率主要在低振幅区域中可见。此外，使用伪Hessian算子可以加快算法的收敛速度，这表明反问题的条件更好。使用合成冥王星模型进行的实验表明，我们的算法大大减少了所需的迭代次数，同时显着提高了图像分辨率，并改善了不良光照事件的连续性。我们通过应用DD方法检索更好的反射率来加快共轭梯度求解器的收敛速度，该反射率主要在低振幅区域中可见。此外，使用伪Hessian算子可以加快算法的收敛速度，这表明反问题的条件更好。使用合成冥王星模型进行的实验表明，我们的算法大大减少了所需的迭代次数，同时显着提高了图像分辨率，并改善了不良光照事件的连续性。