A regularization method for nonlinear inverse problems by using a Volterra model

AbstractVariational source conditions proved to be useful for deriving convergence rates for Tikhonov’s regularization method and also for other methods. Up to now, such conditions have been verified only for few examples or for situations which can be also handled by classical range-type source conditions. Here we show that for almost every ill-posed inverse problem variational source conditions are satisfied. Whether linear or nonlinear, whether Hilbert or Banach spaces, whether one or multiple solutions, variational source conditions are a universal tool for proving convergence rates.

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An iteration regularization method with general convex penalty for nonlinear inverse problems in Banach spaces

Journal of Computational and Applied Mathematics ◽

10.1016/j.cam.2019.05.008 ◽

2019 ◽

Vol 361 ◽

pp. 472-486 ◽

Cited By ~ 5

Author(s):

Jing Wang ◽

Wei Wang ◽

Bo Han

Keyword(s):

Inverse Problems ◽

Banach Spaces ◽

Regularization Method ◽

Nonlinear Inverse Problems ◽

General Convex

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Projection methods and applications for seismic nonlinear inverse problems with multiple constraints

Geophysics ◽

10.1190/geo2018-0192.1 ◽

2019 ◽

Vol 84 (2) ◽

pp. R251-R269 ◽

Cited By ~ 7

Author(s):

Bas Peters ◽

Brendan R. Smithyman ◽

Felix J. Herrmann

Keyword(s):

Inverse Problems ◽

Prior Information ◽

Waveform Inversion ◽

Projection Methods ◽

Multiple Constraints ◽

Local Minima ◽

Low Frequencies ◽

Trade Off ◽

Nonlinear Inverse Problems ◽

Easy Integration

Nonlinear inverse problems are often hampered by local minima because of missing low frequencies and far offsets in the data, lack of access to good starting models, noise, and modeling errors. A well-known approach to counter these deficiencies is to include prior information on the unknown model, which regularizes the inverse problem. Although conventional regularization methods have resulted in enormous progress in ill-posed (geophysical) inverse problems, challenges remain when the prior information consists of multiple pieces. To handle this situation, we have developed an optimization framework that allows us to add multiple pieces of prior information in the form of constraints. The proposed framework is more suitable for full-waveform inversion (FWI) because it offers assurances that multiple constraints are imposed uniquely at each iteration, irrespective of the order in which they are invoked. To project onto the intersection of multiple sets uniquely, we use Dykstra’s algorithm that does not rely on trade-off parameters. In that sense, our approach differs substantially from approaches, such as Tikhonov/penalty regularization and gradient filtering. None of these offer assurances, which makes them less suitable to FWI, where unrealistic intermediate results effectively derail the inversion. By working with intersections of sets, we avoid trade-off parameters and keep objective calculations separate from projections that are often much faster to compute than objectives/gradients in 3D. These features allow for easy integration into existing code bases. Working with constraints also allows for heuristics, where we built up the complexity of the model by a gradual relaxation of the constraints. This strategy helps to avoid convergence to local minima that represent unrealistic models. Using multiple constraints, we obtain better FWI results compared with a quadratic penalty method, whereas all definitions of the constraints are in terms of physical units and follow from the prior knowledge directly.

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