Anderson Acceleration for Seismic Inversion
Yunan Yang

TL;DR
This paper introduces Anderson acceleration to speed up seismic inversion algorithms like FWI and LSRTM, reducing iteration count and computational cost while maintaining accuracy.
Contribution
It innovatively applies Anderson acceleration to seismic inversion, demonstrating significant convergence speedup and computational efficiency improvements over traditional methods.
Findings
AA accelerates convergence in seismic inversion tasks.
AA achieves comparable results to quasi-Newton methods.
Numerical examples show improved efficiency on Marmousi benchmark.
Abstract
The state-of-art seismic imaging techniques treat inversion tasks such as FWI and LSRTM as PDE-constrained optimization problems. Due to the large-scale nature, gradient-based optimization algorithms are preferred in practice to update the model iteratively. Higher-order methods converge in fewer iterations but often require higher computational costs, more line search steps, and bigger memory storage. A balance among these aspects has to be considered. We propose using Anderson acceleration (AA), a popular strategy to speed up the convergence of fixed-point iterations, to accelerate the steepest descent algorithm, which we innovatively treat as a fixed-point iteration. Independent of the dimensionality of the unknown parameters, the computational cost of implementing the method can be reduced to an extremely low-dimensional least-squares problem. The cost can be further reduced by a…
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Taxonomy
TopicsSeismic Imaging and Inversion Techniques · Reservoir Engineering and Simulation Methods · Medical Imaging Techniques and Applications
