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81. Rojas, M., and D. Sorensen, A trust-region approach to the regularization of large-scale
discrete ill-posed problems, TR99-26, Department of Computational and Applied Math-
ematics, Rice University, 1999.
82. Rothman, D. H., Nonlinear inversion, statistical mechanics, and residual statics estimation,
Geophysics, 50, 2784-2796, 1985.
83. Rothman, D. H., Automatic estimation of large residual statics correction, Geophysics,
51, 332-346, 1986.
84. Rudin, L. I., S. Osher, and E. Fatemi, Nonlinear total variation based noise removal
algorithms, Physica D, 60, 259-268, 1992.
85. Sambridge, M., and G. Drijkoningen, Genetic algorithms in seismic waveform inversion,
Geophys, J. Int., 109, 323-342, 1992.
86. Sambridge, M., and K. Gallagher, Earthquake hypocenter location using genetic algo-
rithms, Bull. Seism. Soc. Am., 83, 1467-1491, 1993.
87. Scales, J. A., M. L. Smith, and T. L. Fischer, Global optimization methods for multimodal
inverse problems, J. Comput. Phys., 103, 258-268, 1992.
88. Sen, M. K., and P. L. Stoffa, Nonlinear one-dimensional seismic waveform inversion using
simulated annealing, Geophysics, 56, 1624-1638, 1991.
89. Sen, M. K., and P. L. Stoffa, Rapid sampling of model space using genetic algorithms:
examples from seismic waveform inversion, Geophys, J. Int., 108, 281-292, 1992.
90. Sen, M. K., and P. L. Stoffa, Global Optimization Methods in Geophysical Inversion, El-
sevier, Amsterdam, 281 pp., 1995.
91. Sen, M. K., and P. L. Stoffa, Bayesian inference, Gibbs sampler and uncertainty estimation
in geophysical inversion, Geophys. Prospect., 44, 313-350, 1996.
92. Sluis, A. van der, and H. A. van der Vorst, Numerical solution of large, sparse linear
algebraic systems arising from tomographic problems, p. 49-83 in Seismic Tomography,
G. Nolet (ed.), D. Reidel, Dordrecht, 1987.
93. Stephens C. P. and W. Baritompa, Global optimization requires global information, Jour-
nal of Optimization Theory and Applications, 96, 575-588, 1998.
94. Stoffa, P. L., and M. K. Sen, Nonlinear multiparameter optimization using genetic algo-
rithms: inversion of plane-wave seismograms, Geophysics, 56, 1794-1810, 1991.
95. Tarantola, A., Inverse Problem Theory, Elsevier, 1987.
96. Tarantola, A., Probabilistic foundations of inverse theory, in Geophysical Tomography, Y.
Desaubies, A. Tarantola, and J. Zinn-Justin (eds.), North Holland, 1990.
97. Tolstoy, A., 0. Diachok, and L. N. Frazer, Acoustic tomography via matched field pro-
cessing, J. Acoust. Soc. Am., 89, 1119-1127, 1991.
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Barhen, J.; Berryman, J.G.; Borcea, L.; Dennis, J.; de Groot-Hedlin, C.; Gilbert, F. et al. Optimization and geophysical inverse problems, report, October 1, 2000; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc901685/m1/36/: accessed January 21, 2019), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.