Structure Estimation of 2D Listric Faults Using Quadratic Bezier Curve for Depth Varying Density Distributions

Abstract

Listric faults were first introduced by Suess (1909) for describing faults in coal mines in northern France. The fault planes of listric faults are generally upward concave in nature, and the dip decreases with depth (Shelton, 1984). Listric faults have particular importance in the formation of sedimentary basins. Most of the listric faults are generally occurs during the formation of rift or formation of passive continental margins (Bally et al., 1981). The curvature occurred due to the thick sediment depositions in case of boundary faults (Chakravarthi, 2011). Listric fault can produce structural trap by relative displacement of strata to create a barrier to petroleum migration (Sheth, 1998;Yamada & McClay, 2003). It also has structural importance for mineral explorations (Song et al., 2012).The gravity method is one of the oldest geophysical approaches for subsurface imaging. In general, gravity inversion for subsurface parameter estimation is non-unique but by incorporating proper constraints (Florio, 2020;Li & Oldenburg, 1996;Portniaguine & Zhdanov, 2002) a stable and converging parameter optimization can be achieved. In our present study, the density contrast is assumed to be known from borehole logging and used as a constraint for fault structure estimation. Furthermore, an uncertainty appraisal provides a reliable solution for any ill-posed problem.The gravity method is one of the passive geophysical techniques to study the interior of the Earth. The ground gravity survey is very fast, inexpensive, and can cover a large study area via non-destructive measurements. The gravity method plays a vital role in geological structure estimation and exploration purposes. There are numerous implementations, such as, structure estimation of sedimentary basins (

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

“…However, due to the presence of noise and the nonlinear relationship between the model parameters and the data, these techniques, which primarily aim to find the best solution in a small region of the solution space around the starting point, require prior information and some constraints to determine an appropriate model estimate (Meju, 1994; Menke, 1989; Tarantola, 2005; Zhdanov, 2002). Therefore, global optimization using metaheuristics has gained prominence in geophysics over gradient‐based local inversion in the last decade (Balkaya, 2013; Balkaya et al., 2017; Biswas et al., 2017; Chandra et al., 2017; Ekinci et al., 2016, 2021; Essa & Elhussein, 2018, 2020; Göktürkler et al., 2016; Pace et al., 2019; Pallero et al., 2021; Roy et al., 2022; Santilano et al., 2018; Singh & Biswas, 2016; Sungkono, 2020). These algorithms are designed to search the entire solution space and can handle unconstrained optimization problems (Robert & Casella, 2005; Sen & Stoffa, 2013).…”

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

“…However, because of the well‐known ill‐posedness and non‐uniqueness nature of the geomagnetic data inversion problem, explanation of anomaly sources, that is, model parameter estimations, necessitate some special strategies and efficient approaches (Ekinci et al., 2019). Over the recent years, instead of derivative‐based local optimizers, derivative‐free nature‐inspired global optimizers and metaheuristics such as Particle Swarm Optimization (PSO) (Essa, Abo‐Ezz, et al., 2022; Essa & Elhussein, 2020; Fernández‐Martínez et al., 2010; Pallero et al., 2015; Roy et al., 2022; Santos, 2010), Very Fast Simulated Annealing (VFSA) (Biswas, 2016; Biswas & Acharya, 2016; Biswas & Rao, 2021), Ant Colony Optimization (Liu et al., 2014, 2015; Srivastava et al., 2014); Gray Wolf Optimizer (Agarwal et al., 2018; Chandra et al., 2017), Genetic‐Price Algorithm (Di Maio et al., 2020), Cuckoo Search Algorithm (Turan‐Karaoğlan & Göktürkler, 2021), Differential Search Algorithm (Alkan & Balkaya, 2018; A. 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 (Ekinci, Balkaya, & Göktürkler, 2021), Manta‐Ray Foraging Optimization and Social Spider Optimization (Ben et al., 2022a, 2022b, 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).…”

“…However, because of the well-known ill-posedness and non-uniqueness nature of the geomagnetic data inversion problem, explanation of anomaly sources, that is, model parameter estimations, necessitate some special strategies and efficient approaches (Ekinci et al, 2019). Over the recent years, instead of derivative-based local optimizers, derivative-free nature-inspired global optimizers and metaheuristics such as Particle Swarm Optimization (PSO) (Essa, Abo-Ezz, et al, 2022;Essa & Elhussein, 2020;Fernández-Martínez et al, 2010;Pallero et al, 2015;Roy et al, 2022;Santos, 2010), Very Fast Simulated Annealing (VFSA) (Biswas, 2016;Biswas & Acharya, 2016;Biswas & Rao, 2021), Ant Colony Optimization (Liu et al, 2014(Liu et al, , 2015Srivastava et al, 2014); Gray Wolf Optimizer (Agarwal et al, 2018;Chandra et al, 2017), Genetic-Price Algorithm (Di Maio et al, 2020), Cuckoo Search Algorithm (Turan-Karaoğlan & Göktürkler, 2021), Differential Search Algorithm (Alkan & Balkaya, 2018;A. Balkaya & Kaftan, 2021;Özyalın & Sındırgı, 2023), Bat Algorithm (Essa & Diab, 2022;Gobashy et al, 2021), Differential Evolution Algorithm (Ç.…”