Randomized linear algebra for inversion

TitleRandomized linear algebra for inversion
Publication TypePresentation
Year of Publication2021
AuthorsMathias Louboutin, Felix J. Herrmann
KeywordsFWI, HPC, inversion, ML4SEISMIC, randomized linear algebra, SLIM, software

Inverse problems in exploration geophysics or machine learning heavily relies on linear algebra and large matrices manipulations. To tackle the growing cost of storing these matrices, randomized algorithms have been developed to obtain information from these matrices via randomized sketching. Inspired by previous work on extended image volumes, we will first show in this talk how the seismic imaging condition can be expressed in a randomized linear algebra framework leading to drastic memory savings. In a second part, we will extend this idea to convolutional neural networks to reduce the memory cost of training by orders of magnitude. We will demonstrate the practicality of these methods on representative examples.

Citation Keylouboutin2021ML4SEISMICrla