Source-receiver Compression Approach For 3D Full-waveform Inversion With an Iterative Forward Solver

We apply a source-receiver compression approach to reduce the computational time and memory usage of threedimensional acoustic full-waveform inversion. By detecting and quantifying the extent of redundancy in the data, we assemble a reduced set of simultaneous sources and receivers that are weighted sums of the physical sources and receivers used in the survey. Because the number of these simultaneous sources and receivers can be significantly less than those of the physical sources and receivers, the computational time and memory usage of any inversion method can be tremendously reduced. The scheme is based on decomposing the data into their principal components using a singular value decomposition scheme and the data reduction is done through the elimination of the small eigenvalues. For the inversion method, we employ the multiplicative-regularized Gauss-Newton approach, in which the regularization parameter is automatically and adaptively determined by the inversion process. For the forward solver, we employ a frequency-domain iterative solver using the perfectly matched layer absorbing boundary condition and a multigrid preconditioner. We demonstrate the advantage of our approach by using the 3D SEG/EAGE salt model.