scholarly journals Lamé Parameter Estimation from Static Displacement Field Measurements in the Framework of Nonlinear Inverse Problems

2018 ◽  
Vol 11 (2) ◽  
pp. 1268-1293 ◽  
Author(s):  
Simon Hubmer ◽  
Ekaterina Sherina ◽  
Andreas Neubauer ◽  
Otmar Scherzer
Keyword(s):  
Parameter Estimation ◽  
Inverse Problems ◽  
Displacement Field ◽  
Field Measurements ◽  
Static Displacement ◽  
Nonlinear Inverse Problems

scholarly journals Multiscale displacement field measurements of compressed mineral-wool samples by digital image correlation

2002 ◽  
Vol 41 (32) ◽  
pp. 6815 ◽  
Author(s):  
François Hild ◽  
Bumedijen Raka ◽  
Maud Baudequin ◽  
Stéphane Roux ◽  
Florence Cantelaube
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
Displacement Field ◽  
Field Measurements ◽  
Mineral Wool ◽  
Image Correlation

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

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. R251-R269 ◽  
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.

On convergence rates for asymptotic discrepancy principle

2016 ◽  
Vol 24 (4) ◽  
Author(s):  
Anatoly Bakushinsky ◽  
Alexandra Smirnova
Keyword(s):  
Parameter Estimation ◽  
Inverse Problems ◽  
Numerical Experiments ◽  
Convergence Rates ◽  
Discrepancy Principle

AbstractA series of recent numerical experiments for parameter estimation inverse problems in epidemiology [

Effects of model discretization on the statistics of model parameters in nonlinear inverse problems

2007 ◽  
Vol 121 (5) ◽  
pp. 3125-3125
Author(s):  
Andrew A. Ganse ◽  
Robert I. Odom ◽  
Andrew A. Ganse ◽  
Robert I. Odom
Keyword(s):  
Inverse Problems ◽  
Model Parameters ◽  
Nonlinear Inverse Problems
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