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Fast non-convex low-rank matrix decomposition for separation of potential field data using minimal memory
Inverse Problems and Imaging
A fast non-convex low-rank matrix decomposition method for potential field data separation is presented. The singular value decomposition of the large size trajectory matrix, which is also a block Hankel matrix, is obtained using a fast randomized singular value decomposition algorithm in which fast block Hankel matrix-vector multiplications are implemented with minimal memory storage. This fast block Hankel matrix randomized singular value decomposition algorithm is integrated into the Altprojdoi:10.3934/ipi.2020076 fatcat:uozh33maszgytb5olmbvulgkem