1998
DOI: 10.1029/1998gl900063
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Delineation of sediments below flood basalts by joint inversion of seismic and magnetotelluric data

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Cited by 19 publications
(14 citation statements)
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“…Synthetic tests show it better resolves layer interfaces than the separate inversion of each data set, thus reducing some non-uniqueness in the solution. A similar approach has been used by Manglik and Verma (1998) for the 1D inversion of magnetotelluric, seismic refraction, and seismic reflection data. These 1D approaches can be viewed as a type of adaptive mesh inversion, in which the inversion parameters include the model mesh itself (layer thicknesses).…”
Section: Constrained Multi-property Inversionmentioning
confidence: 97%
“…Synthetic tests show it better resolves layer interfaces than the separate inversion of each data set, thus reducing some non-uniqueness in the solution. A similar approach has been used by Manglik and Verma (1998) for the 1D inversion of magnetotelluric, seismic refraction, and seismic reflection data. These 1D approaches can be viewed as a type of adaptive mesh inversion, in which the inversion parameters include the model mesh itself (layer thicknesses).…”
Section: Constrained Multi-property Inversionmentioning
confidence: 97%
“…The joint inversion considers several datasets simultaneously by a numerical scheme that iteratively modifies a common starting model until an optimum model satisfying all datasets simultaneously within a specified error tolerance is obtained. Earlier, Manglik and Verma (1998) developed a one-dimensional joint inversion scheme combining MT and seismic reflection (RFLS) and refraction (RFRS) travel time data. Manglik and Verma (1999) included DC resistivity inversion to their joint MT -seismic inversion algorithm.…”
Section: Introductionmentioning
confidence: 99%
“…Addition of large damping factor stabilizes the matrix by enhancing small eigenvalues but at the cost of losing some information. It is, therefore essential to keep the damping factor as small as possible as one approaches final solution (Manglik and Verma 1998). In the present work the damping factor is changed in an iterative manner from 0.001 to 0.025 in 10 iteration and final k for a particular inversion is selected on the basis of root mean square error in data parameters as shown below:…”
Section: Mathematical Formulation Of the Problemmentioning
confidence: 99%