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<p>When separate inversion of geophysical data set cannot result in desirable reconstruction of subsurface anomalies, joint inversion of geophysical methods is one of the most common ways to overcome the problem. Cross gradient based joint inversion is the only technique allowing to execute joint inversion of magnetometry and DC resistivity data. In this paper, joint inversion of magnetometry and DC resistivity, based on nonlinear equations, is carried out. Smoothness and product of depth weighting and compactness are manipulated as model weighting functions for DC resistivity and magnetometry methods, respectively. The joint inversion is first applied to noise-free synthetic data of a thin dyke to evaluate its productivity which shows that separate inversion of DC resistivity leads to poor reconstruction of the thin dyke, while magnetometry recovers the dyke model very well. After&#xa0;the joint procedure, the&#xa0;magnetic method plays the role of an efficient constraint through cross gradient to improve the resistivity model about satisfactory recovery of the thin dyke. Then, usefulness of the joint inverse algorithm is tested in the presence of the noise demonstrating its productivity for noise levels up to 5%. Ultimately, the joint inversion algorithm is utilized for the real data collected over a relatively thin dyke in Morgenzon Farm in South Africa successfully.</p>

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Structural based joint inversion of magnetometry and DC resistivity data through cross gradient constraint

  • 期刊论文

  • 发表日期：

    2025年1月23日

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  • Ali Dehghan Mongabadi,
  • Ahmad Zarean Shirvanehdeh,
  • Aynur Nasseri,
  • Rashed Pourmirzaee

摘要

When separate inversion of geophysical data set cannot result in desirable reconstruction of subsurface anomalies, joint inversion of geophysical methods is one of the most common ways to overcome the problem. Cross gradient based joint inversion is the only technique allowing to execute joint inversion of magnetometry and DC resistivity data. In this paper, joint inversion of magnetometry and DC resistivity, based on nonlinear equations, is carried out. Smoothness and product of depth weighting and compactness are manipulated as model weighting functions for DC resistivity and magnetometry methods, respectively. The joint inversion is first applied to noise-free synthetic data of a thin dyke to evaluate its productivity which shows that separate inversion of DC resistivity leads to poor reconstruction of the thin dyke, while magnetometry recovers the dyke model very well. After the joint procedure, the magnetic method plays the role of an efficient constraint through cross gradient to improve the resistivity model about satisfactory recovery of the thin dyke. Then, usefulness of the joint inverse algorithm is tested in the presence of the noise demonstrating its productivity for noise levels up to 5%. Ultimately, the joint inversion algorithm is utilized for the real data collected over a relatively thin dyke in Morgenzon Farm in South Africa successfully.