3-D gravity inversion for the basement relief reconstruction through modified success-history-based adaptive differential evolution

Abstract

Summary A gravity inversion procedure using the success–history–based adaptive differential evolution (SHADE) algorithm is presented to reconstruct the 3D basement relief geometry in sedimentary basins. We introduced exponential population size (number) reduction (EPSR) to reduce the computational cost and used self–adaptive control parameters to solve this highly nonlinear inverse problem. Model parameterization was carried out by discretizing the sedimentary cover via juxtaposed right prisms, each placed below each observation point. Resolvability characteristics of the 3D inverse problem were revealed through some cost function topography landscapes. The fine–tuned control parameter namely, population number allowed us to get best benefit from the algorithm. Additionally, a stabilizing function as a relative constraint was used to avoid undesired effects originated from the ill–posedness of the problem. In the synthetic data cases, the strategy we propose outperformed the linear population number reduction (LPSR) strategy which has won various IEEE–CEC competitions so far. Thorough uncertainty assessments via probability density function (PDF) and principal component analysis (PCA) demonstrated the solidity of the obtained inverse models. In the real data case, residual gravity anomalies of two well–known major grabens of Aegean Graben System (Türkiye), calculated thanks to the finite element method (FEM), were inverted. It was determined that the inverse solutions obtained for these basement reliefs, whose depths are still controversial, are statistically reliable. Moreover, these depths were found to be less than the depths reported in most previous studies. We conclude that the SHADE using EPSR strategy that we propose is a powerful alternative inversion tool for highly nonlinear geophysical problems.

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

“…Applying a principal component analysis (PCA) to the posterior samples can reduce dimensionality in the parameter space and allow a more explicit representation and analysis of the posterior distribution for many model parameters on two axes. For this reason, posterior analysis with PCA has also been performed recently to investigate the reliability of model solutions obtained from the inversion of geophysical anomalies (Ekinci et al., 2023; Pallero et al., 2015, 2017; Roy et al., 2021; Sungkono et al., 2023). In the present case, PCA essentially involves the computation of eigenvectors and eigenvalues from the covariance matrix of the model solution matrix obtained by the applied optimization approaches.…”

“…10.1029/2023EA003082 parameters on two axes. For this reason, posterior analysis with PCA has also been performed recently to investigate the reliability of model solutions obtained from the inversion of geophysical anomalies (Ekinci et al, 2023;Pallero et al, 2015Pallero et al, , 2017Roy et al, 2021;Sungkono et al, 2023). In the present case, PCA essentially involves the computation of eigenvectors and eigenvalues from the covariance matrix of the model solution matrix obtained by the applied optimization approaches.…”

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

“…Balkaya & Kaftan, 2021;Özyalın & Sındırgı, 2023), Bat Algorithm (Essa & Diab, 2022;Gobashy et al, 2021), Differential Evolution Algorithm (Ç. Balkaya, 2013;Du et al, 2021;Ekinci, Balkaya, & Göktürkler, 2020;Ekinci et al, 2023;Göktürkler et al, 2016;Hosseinzadeh et al, 2023;Roy et al, 2021a;Sungkono, 2020); Backtracking Search Algorithm , Manta-Ray Foraging Optimization and Social Spider Optimization (Ben et al, 2022a(Ben et al, , 2022b(Ben et al, , 2022c, Barnacles Mating Optimization (BMO) (Ai et al, 2022) have gained increasing attention in geophysical inversion applications. Unlike local search algorithms, these stochastic optimizers do not need a well-designed starting point in the model space to reach the global minimum (Sen & Stoffa, 2013;Tarantola, 2005).…”