A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems

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Comparing parameter choice methods for regularization of ill-posed problems (2011, methodology): “Considering the number of parameter choice methods, there are relatively few comparative studies of them [1] [3] [9] [29] [45] [60] [63] [67] [89] [101] [113] [125] [131] [141], [71, chap.”
Fast nonlinear gravity inversion in spherical coordinates with application to the South American Moho (2017, background): “Farquharson & Oldenburg (2004) provide for a thorough comparison of both methods.”
Parallel computation of optimized arrays for 2-D electrical imaging surveys (2010, methodology): “Figs 7e and f show the inversion models for the dipole–dipole array and optimized data sets using the L-curve method (Farquharson and Oldenburg 2004) to automatically select the damping factor.”
SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications (2015, methodology): “…the L-curve technique (Hansen, 1992), which attempts to find the point of greatest curvature in the Tikhonov curve; and Generalized Cross Validation (Wahba, 1990; Golub et al., 1979; Golub and Von Matt, 1997; Haber and Oldenburg, 2000; Oldenburg and Li, 2005; Farquharson and Oldenburg, 2004).”
Three‐dimensional modelling and inversion of dc resistivity data incorporating topography – II. Inversion (2006, methodology): “There exist several methods to compromise data fit and model constraints (Farquharson & Oldenburg 2004).”