IGUG: A MATLAB package for 3D inversion of gravity data using graph theory

Vatankhah, Saeed; Ardestani, Vahid E.; Niri, Susan Soodmand; Renaut, Rosemary A.; Kabirzadeh, Hojjat

doi:10.1016/j.cageo.2019.03.008

Cited by 12 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the studies shown in [10,15,56,57], the objective function for gravity inversion is comprised of data misfit and model misfit. By introducing regularization factor λ, the objective function is written as:…”

Section: Gravity Inversion With Multiplicative Regularization 231 Inv...mentioning

confidence: 99%

Inversion of Gravity Data with Multiplicative Regularization Using an Improved Adaptive Differential Evolution

Cheng,

Xiao,

et al. 2023

Minerals

View full text Add to dashboard Cite

Differential evolution (DE) is a stochastic optimization technique that imitates the evolution process in nature. This paper uses an improved adaptive differential evolution to solve gravity inversion with multiplicative regularization. Compared with additive regularization, the advantage of multiplicative regularization is that it does not require the regularization parameter in the search process. The contributions in this paper mainly focus on two aspects: accelerating the convergence speed of adaptive DE and balancing the effect of model and data misfits in the objective function. The effectiveness of the proposed inversion method is verified by synthetic and field cases. For the synthetic cases, it is concluded that, based on the obtained results and analysis, the presented DE method is superior and competitive with its original version. Additionally, the designed parameter adaptation for multiplicative regularization is useful for trading off the effect of data and model misfits. For the field cases, two successful applications from China were conducted, and the obtained density source distributions were in accordance with those obtained from drilling wells. The synthetic and practical examples demonstrate that high-quality inversion results can be obtained using improved adaptive differential evolution and multiplicative regularization.

show abstract

Section: Gravity Inversion With Multiplicative Regularization 231 Inv...mentioning

confidence: 99%

Inversion of Gravity Data with Multiplicative Regularization Using an Improved Adaptive Differential Evolution

Cheng,

Xiao,

et al. 2023

Minerals

View full text Add to dashboard Cite

show abstract

“…The same observed gravity data can be obtained by different sub-surface density distributions (Blakely, 1996;Rezaie et al, 2017). Therefore, many effective methods with addition constraint on density model are studied to obtain a reasonable and accuracy solution (Boulanger and Chouteau, 2001;Silva and Barbosa, 2006;Commer, 2011;Vatankhah et al, 2017;Vatankhah et al, 2018;Vatankhah et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The improvement of sparsity gravity inversion using an adaptive lanczos bidiagonalization method

Meng

Wang²,

Li³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

Inversion of gravity data is one the important steps in the interpretation of practical data. The detection of sharp boundaries between anomalous bodies and host rocks is an interesting point in the geological frameworks. The gravity inversion with sparsity constraint is a useful method to recover block subsurface density distribution, which is efficiently used for the quantitative interpretation of gravity data. The reweighted regularized method is a useful method to solve the inverse problem. However, in this type, we must face the updating gravity forward matrix and large matrix operation. The application of Lanczos bidiagonalization method can reduce the size of data and matrix in the inversion to resolve the large scale inversion problem. However, a very important problem is not resolved, which is update of reweighted forward matrix and new Lanczos bidiagonalization matrix. Here, an adaptive Lanczos bidiagonalization method is studied to select the Lanczos bidiagonalization factor. And a new projected method with adaptive Lanczos bidiagonalization method is study to avoid the updating sparsity reweighted function. We calculate the reweighted forward matrix and Lanczos bidiagonalization matrix only one time, which can essentially reduce the computational complexity. The inversion results of synthetic data show that the new improved method is faster and better than common reweight regularized Lanczos bidiagonalization method to produce an acceptable solution for focusing inverse problem. The improvement of adaptive Lanczos bidiagonalization in sparsity gravity inversion is also tested on gravity data collected over the Mobrun massive sulfide ore body in Noranda, Quebec, Canada. The inversion results indicate a remarkable correlation with true structure of the ore body that is achieved from drilling data.

show abstract

Upgraded software for structural gravity inversion and application to El Hierro (Canary Islands)

Camacho

Prieto

Aparicio

et al. 2021

Computers & Geosciences

View full text Add to dashboard Cite

IGUG: A MATLAB package for 3D inversion of gravity data using graph theory

Cited by 12 publications

References 19 publications

Inversion of Gravity Data with Multiplicative Regularization Using an Improved Adaptive Differential Evolution

Inversion of Gravity Data with Multiplicative Regularization Using an Improved Adaptive Differential Evolution

The improvement of sparsity gravity inversion using an adaptive lanczos bidiagonalization method

Upgraded software for structural gravity inversion and application to El Hierro (Canary Islands)

Contact Info

Product

Resources

About