2019
DOI: 10.1002/nsg.12080
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A sigmoid stabilizing function for fast sparse 3D inversion of magnetic data

Abstract: An interesting geological objective of quantitative interpretation of magnetic data is to find inverse models which can determine sharp geological interfaces below the surface. The stabilizing function in the Tikhonov parametric functional governs sparseness constraint in the recovered model. This paper introduces a novel stabilizer based on a sigmoid function which can provide non‐smooth models in the inversion of magnetic data efficiently. An inversion algorithm is developed based on the reweighted regulariz… Show more

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Cited by 13 publications
(2 citation statements)
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“…In one such approach, researchers proposed the non-smooth stabilizer to replace the general smooth stabilizer and identified the physical bounds from a priori information to obtain results with sharp boundaries, such as the minimum support stabilizer, known as the L 0 -norm stabilizer [10], the minimum gradient support stabilizer [11], the L 1 -norm stabilizer [8], the Cauchy norm stabilizer [12], the zero-order minimum entropy stabilizer [13], the sigmoid stabilizing function [14], and the error function stabilizer [15]. Another approach to improve the inversion resolution of gravity and magnetic data is the joint inversion of multiple geophysical data.…”
Section: Of 17mentioning
confidence: 99%
“…In one such approach, researchers proposed the non-smooth stabilizer to replace the general smooth stabilizer and identified the physical bounds from a priori information to obtain results with sharp boundaries, such as the minimum support stabilizer, known as the L 0 -norm stabilizer [10], the minimum gradient support stabilizer [11], the L 1 -norm stabilizer [8], the Cauchy norm stabilizer [12], the zero-order minimum entropy stabilizer [13], the sigmoid stabilizing function [14], and the error function stabilizer [15]. Another approach to improve the inversion resolution of gravity and magnetic data is the joint inversion of multiple geophysical data.…”
Section: Of 17mentioning
confidence: 99%
“…By joining GA global scheme and ray tracing, we intend to (i) estimate a good initial model of the velocity field by inversion and (ii) understand the dependence of GA with sigmoidal parameterization. Both techniques has been widespread in literature (DOCHERTY et al, 1997;FERREIRA;PORSANI;OLIVEIRA, 2003;REZAIE, 2020;SAM-BRIDGE;DRIJKONINGEN, 1992) and will be used to determine the velocity field coefficients given by the sigmoidal parameterization making this work a novelty. This methodology is useful since it can be applied to any set of traveltimes where forward modeling is effective, regardless of the model geometry or data set to be used.…”
Section: Introductionmentioning
confidence: 99%