WaRIance：随机变量投影波场重建反演
WaRIance: wavefield reconstruction inversion with stochastic variable projection
摘要：
我们提出了一种变化的波场重建反演地震反演，它利用随机线性代数作为一种方式，以克服传统的反演技术的典型局限性。因此，我们可以同时针对收敛停滞和大型3D应用的鲁棒性。其中心思想在于通过对波动方程误差协方差的低秩随机逼近来近似波场重建反演中所涉及的最佳松弛变量。其结果是，我们得到了一个家庭的反演方法参数化的一个给定的模型协方差（适合手头的问题）和相关的随机逼近草图的秩。我们的建议的挑战和优势，证明了一些数值实验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
