Gravity data inversion for the basement relief delineation through global optimization: a case study from the Aegean Graben System, western Anatolia, Turkey

Abstract

Summary Aegean Graben System is a significant member of the complex geology of western Turkey. The depths to the metamorphic basement reliefs in two major grabens of this system have been reported by many geophysical studies so far. However, the continental sediment thicknesses of these graben basins still remain controversial due to the findings differing from each other. Thus, an attempt has been made here to invert the gravity data of the sedimentary cover–metamorphic basement using a stochastic derivative-free vector-based metaheuristic named differential evolution algorithm (DEA). This is the first application of DEA adapted to the basement relief depth problem. Model parameterizations have been achieved by discretizing the basins using a group of juxtaposed vertical blocks. Before the inversion studies, mathematical nature of the inverse problem has been investigated by producing prediction cost function/error energy maps for some block pairs using a hypothetical symmetric basin model. These maps have shown the resolvability characteristic of the block thicknesses on such inversion problem. Parameter tuning studies for the optimum mutation constant/weighting factor have been also performed to increase the efficiency of the algorithm. The synthetic data have been successfully inverted via the tuned control parameter and some smoothing operators. Probability density function (PDF) analyses have shown that the best solutions of independent runs are within the confidence interval limits without model uncertainties. In the field data case, long-wavelength anomalies originated from both crustal and deeper effects have been removed from the complete Bouguer anomalies through a 2D finite element method (FEM) using the element shape functions (ESFs). Some profiles extracted from the residual gravity anomaly map have been used for the inversion and obtained results have shown that the maximum depths to the metamorphic basement reliefs in the grabens are shallower than the findings of the previous studies. Information obtained from the lithological logs drilled in the grabens has supported our results. Moreover, probability density function analyses have also indicated the reliability of the obtained sedimentary package thicknesses without uncertainties.

GravPSO2D: A Matlab package for 2D gravity inversion in sedimentary basins using the Particle Swarm Optimization algorithm

“…The 2D basin modeling used in GravPSO2D consists in the juxtaposition of rectangles along the profile as it was commonly employed by other authors (see for example Silva et al (2009) or Ekinci et al (2020)). As it can be seen in Fig.…”

“…In Singh and Singh (2017) PSO is applied to a 2.5D gravity inversion in sedimentary basins, although in this case only comparisons with results obtained via the Marquardt method are performed. Finally, Ekinci et al (2020) have recently used a different global optimization method, called differential evolution algorithm (DEA), to a 2D approximation of this kind of problem applied to the Aegean Graben System (Turkey), performing algo uncertainty analysis.…”

Time-dependent model for earthquake occurrence and effects of design spectra on structural performance: a case study from the North Anatolian Fault Zone, Turkey

“…Seismogenic zone 6 covers the NW-SE striking Erzincan basin which appears to be a major step over along the NAFZ (Şengör, 1979;Hubert-Ferrari et al, 2002). The largest events in this seismogenic Okay and Tüysüz, 1999;Yiğitbaş et al, 2004;USGS, 2010;Ekinci et al, 2020Ekinci et al, , 2021Işık et al, 2020 We used instrumental (M S ≥ 5.5, until the end of 2019) and historical data with maximum intensities of I 0 ≥ 9.0 corresponding to surface wave magnitude M S ≥ 7.0 (Sayıl, 2013). Different-scaled magnitudes were transformed to M S using empirical equations obtained from regional earthquakes (Figure 3).…”

“…Figure 1. Tectonic map of Turkey and the surrounding (compiled withOkay and Tüysüz, 1999;Yiğitbaş et al, 2004; USGS, 2010;Ekinci et al, 2020Ekinci et al, , 2021Işık et al, 2020). NAFZ, North Anatolian Fault Zone; EAFZ, East Anatolian Fault Zone; NEAFZ, Northeast Anatolian Fault Zone; BZSZ, Bitlis-Zagros Suture Zone; DSFZ, Dead Sea Fault Zone; WAGS, West Anatolian Graben System; SBST, Southern Black Sea Thrust.…”

Interpretation of Magnetic Anomalies over 2D Fault and Sheet-Type Mineralized Structures Using Very Fast Simulated Annealing Global Optimization: An Understanding of Uncertainty and Geological Implications

“…Due to its limitations in delineating an effective solution, global optimization or metaheuristic optimization is necessary for finding an optimal solution that does not need an initial guess of the model parameters and can give the best result. Global optimization such as the genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE) algorithm, very fast simulated annealing (VFSA), genetic-price algorithm (GPO), and whale optimization algorithm (WOA) has been applied to numerous geophysical applications such as seismic data [56][57][58], self-potential data [59][60][61][62][63][64][65][66][67][68][69][70][71][72][73], and also in the interpretation of gravity and magnetic data [3,[74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92].…”