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Learned Iterative Solvers for the Helmholtz EquationNormal access

Authors: G. Rizzuti, A. Siahkoohi and F.J. Herrmann
Event name: 81st EAGE Conference and Exhibition 2019
Session: Seismic Modelling II
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201901542
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 525.51Kb )
Price: € 20

Summary:
We propose a `learned' iterative solver for the Helmholtz equation, by combining traditional Krylov-based solvers with machine learning. The method is, in principle, able to circumvent the shortcomings of classical iterative solvers, and has clear advantages over purely data-driven approaches. We demonstrate the effectiveness of this approach under a 1.5-D assumption, when adequate a priori information about the velocity distribution is known.


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