KAUST DepartmentKing Abdullah University of Science and Technology (KAUST)
Permanent link to this recordhttp://hdl.handle.net/10754/670963
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AbstractIn the last decade, seismic wavefield processing has begun to rely more heavily on the solution of wave-equation-based inverse problems. Especially when dealing with unfavourable data acquisition conditions (e.g., poor, regular or irregular sampling of sources and/or receivers), the underlying inverse problem is generally very ill-posed; sparsity promoting inversion coupled with fixed-basis sparsifying transforms has become the de-facto approach for many processing algorithms. Motivated by the ability of deep neural networks to identify compact representations of N-dimensional vector spaces, we propose to learn a mapping between the input seismic data and a latent manifold by means of an Autoencoder. The trained decoder is subsequently used as a nonlinear preconditioner for the inverse problem we wish to solve. Using joint deghosting and data reconstruction as an example, we show that nonlinear learned transforms outperform fixed-basis transforms and enable faster convergence to the sought solution (i.e, fewer applications of the forward and adjoint operators are required).
CitationRavasi, M. (2021). Seismic wavefield processing with deep preconditioners. First International Meeting for Applied Geoscience & Energy Expanded Abstracts. doi:10.1190/segam2021-3580609.1
PublisherSociety of Exploration Geophysicists