3‐D magnetic inversion with data compression and image focusing

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Regularized derivatives of potential fields and their role in semi‐automated interpretation methods (2009, methodology): “The widely used concepts (known to the authors) are methods based on the estimation of the error distribution in the input data (e.g., Sacchi and Ulrych 1996; Guspı́ and Introcaso 2000) and finding the optimum value in an iterative process (e.g., Portniaguine and Zhdanov 1999, 2002).”
A hybrid regularized inversion methodology for highly parameterized environmental models (2005, background): “…the adjoint state [Tsourlos and Ogilvy, 1999], and solving the large matrix equations that result from estimating a large number of parameters, such as conjugate gradients [Mackie and Madden, 1993; Haber et al., 2000] and matrix factorizations [Portniaguine, 1999; Portniaguine and Zhdanov, 2002].”
Three-dimensional regularized focusing inversion of gravity gradient tensor component data (2004, methodology): “The minimization prob lem (28) is solved using the reweighted regularize d conjugate gradie nt (RRCG) method , outlined in previous publicat ions (Portniaguine and Zhdanov, 1999, 2002).”