50 citations · 351 across the 23 of their papers we have counts for
23 papers
The Variable Projected Augmented Lagrangian Method
Matthias Chung, Rosemary Renaut
Inference by means of mathematical modeling from a collection of observations remains a crucial tool for scientific discovery and is ubiquitous in application areas such as signal…
Large-scale focusing joint inversion of gravity and magnetic data with Gramian constraint
Saeed Vatankhah, Rosemary A. Renaut, Xingguo Huang +2
A fast algorithm for the large-scale joint inversion of gravity and magnetic data is developed. It uses a nonlinear Gramian constraint to impose correlation between density and sus…
Learning Spectral Windowing Parameters for Regularization Using Unbiased Predictive Risk and Generalized Cross Validation Techniques for Multiple Data Sets
Michael J. Byrne, Rosemary A. Renaut
During the inversion of discrete linear systems noise in data can be amplified and result in meaningless solutions. To combat this effect, characteristics of solutions that are con…
A fast methodology for large-scale focusing inversion of gravity and magnetic data using the structured model matrix and the fast Fourier transform
Rosemary A. Renaut, Jarom D. Hogue, Saeed Vatankhah
Focusing inversion of potential field data for the recovery of sparse subsurface structures from surface measurement data on a uniform grid is discussed. For the uniform grid the m…
Generalized L-norm joint inversion of gravity and magnetic data using cross-gradient constraint
Saeed Vatankhah, Shuang Liu, Rosemary A. Renaut +2
A generalized unifying approach for -norm joint inversion of gravity and magnetic data using the cross-gradient constraint is presented. The presented framework incorporates…
A Tutorial and Open Source Software for the Efficient Evaluation of Gravity and Magnetic Kernels
Jarom D Hogue, Rosemary A Renaut, Saeed Vatankhah
Fast computation of three-dimensional gravity and magnetic forward models is considered. Measurement data is assumed to be obtained on a uniform grid which is staggered with respec…