1982
DOI: 10.4294/jpe1952.30.451
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Generalized least-squares solutions to quasi-linear inverse problems with a priori information.

Abstract: A quasi-linear inverse problem with a priori information about model parameters is formulated in a stochastic framework by using a singular value decomposition technique for arbitrary rectangular matrices. In many geophysical inverse problems, we have a priori information from which a most plausible solution and the statistics of its probable error can be guessed.Starting from the most plausible solution, the optimization of model parameters is made by the successive iteration of solving a set of standardized … Show more

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Cited by 60 publications
(39 citation statements)
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“…(15) is the unique least-squares solution with minimum length of the solution vector for the underdetermined case (e.g. Matsu'ura and Hirata, 1982). Such a solution with minimum overall change in the system under investigation is often physically or chemically seen to be the most appropriate solution ("Occam's razor").…”
Section: The Svd Least-squares Solutionmentioning
confidence: 99%
“…(15) is the unique least-squares solution with minimum length of the solution vector for the underdetermined case (e.g. Matsu'ura and Hirata, 1982). Such a solution with minimum overall change in the system under investigation is often physically or chemically seen to be the most appropriate solution ("Occam's razor").…”
Section: The Svd Least-squares Solutionmentioning
confidence: 99%
“…We used a singular value decomposition method to solve the inverse problem and followed Matsu'ura and Hirata (1982) to determine how many singu-8 lar values to keep in the reconstruction of the model. We inverted the following equations that relate phase velocity anisotropy to azimuthal anisotropy at depth:…”
Section: Forward and Inverse Problemmentioning
confidence: 99%
“…A small n s /n r ratio implies a slower convergence of the algorithm but helps perfom a sampling of the model space as thorough as possible to avoid getting trapped in a In this work, we first carried out regularized inversions of the surface wave data using eq. 15 and the singular value decomposition (SVD) method of Matsu'ura & Hirata (1982). This technique is described in details in .…”
Section: Modelingmentioning
confidence: 99%