We propose a variation on wavefield reconstruction inversion for seismic inversion, which takes advantage of randomized linear algebra as a way to overcome the typical limitations of conventional inversion techniques. Consequently, we can aim both to robustness towards convergence stagnation and large-sized 3D applications. The central idea hinges on approximating the optimal slack variables involved in wavefield reconstruction inversion via a low-rank stochastic approximation of the wave-equation error covariance. As a result, we obtain a family of inversion methods parameterized by a given model covariance (suited for the problem at hand) and the rank of the related stochastic approximation sketch. The challenges and advantages of our proposal are demonstrated with some numerical experiments.

翻译：我们提议对波地重建的变换,以用于地震反向,利用随机的线性代数,以此克服常规反向技术的典型局限性。因此,我们可以将目标放在稳健的趋同停滞和大型的3D应用上。 中心思想在于通过波地重建的低级平差差差共变差近似值,将波地重建中的最佳缓冲变换为近似最佳的变数。结果,我们获得了一组由某种模型共变差(适合手头的问题)和相关的随机近似草图排序参数参数参数参数参数参数参数的反向方法。我们提案的挑战和优势在一些数字实验中得到了证明。