Depth Estimation of Sedimentary Sections and Basement Rocks in the Bornu Basin, Northeast Nigeria Using High-Resolution Airborne Magnetic Data

Abstract

This study involves the use of high-resolution airborne magnetic data to evaluate the thicknesses of sedimentary series in the Bornu Basin, Northeast Nigeria, using three depth approximation techniques (source parameter imaging, standard Euler deconvolution, and 2D GM-SYS forward modelling methods). Three evenly spaced profiles were drawn in the N-S direction on the total magnetic intensity map perpendicular to the regional magnetic structures. These profiles were used to generate three 2-D models. The magnetic signatures were visually assessed to determine the thickness of depo-centres and the position of intrusions. The thicknesses of sedimentary series based on source parameter imaging results are approximately ranged 286 to 615 m, 695 to 1038 m, and 1145 to 5885 m for thin, intermediate, and thick sedimentation, respectively. Similarly, the standard Euler deconvolution result shows thin (130 to 917 m), intermediate (1044 to 1572 m), and thick (1725 to 5974 m) sedimentation. The magnetic model of Profile 1, characterized by two major breaks, shows that the igneous intrusions and basement rocks are covered by sediments with thickness varying from 300 to <3500 m, while Profile 2 has a maximum estimated depth value of about 5000 m at the southern part. Furthermore, the Profile 3 model shows sediment thicknesses of 2500 and 4500 m in the northern and southern flanks of the profile, respectively. The maximum sediment thickness value from the various depth estimation methods used in this study correlate relatively well with each other. Furthermore, the anomalous depth zone revealed by the 2D forward models coincides with the locality of the thick sedimentation revealed by the source parameter imaging and standard Euler-deconvolution (St-ED) methods. The maximum depth values obtained from the various depth estimation methods used in this study correlated strongly with each other. The widespread occurrence of short-wavelength anomalies in the southern part of the study area as indicated by the jagged nature of the magnetic signature was validated by the analytic signal and upward-continuation results. Generally, it was observed that the southern part of the research area is characterized by thick sedimentation and igneous intrusions.

Application of High-Precision Filters on Airborne Magnetic Data: A Case Study of the Ogoja Region, Southeast Nigeria

“…Similar structural trends have been previously observed in the LBT [37,43,44]. These near-surface geologic structures were triggered by the widespread intrusions [60], as well as metamorphosed Albian shales [39,42,43,60] interrelated to the Santonian AA [38]. The related tectonic perturbations caused the high concentration of lineaments in the Ogoja region, which served as pathways for the movements and entrapments of brines [36] and lead-zinc [38] in the region.…”

“…In general, some previous geologic structural studies in the LBT involved filters such as the first and second vertical derivatives, tilt-angle derivative, total horizontal gradient, analytic signal, downward continuation, and center for exploration targeting [37,[41][42][43][44][45]. All these enhancement and filtering procedures permitted the mapping of responses linked to the mineralization, geologic structures, and lithology of the area.…”

An integrated approach of advanced methods for mapping geologic structures and sedimentary thickness in Ukelle and adjoining region (Southeast Nigeria)

“…7). The northwest and southeast flanks that correlated strongly with zones dominated by thin (0-500 m) Cretaceous sedimentary series, are controlled by widespread near-surface igneous, tuffs and calc-alkaline lavas intrusions (Benkhelil, 1987;Murat, 1972) as well as metamorphosed Albian shales (Ekwok et al, 2022c;Benkhelil et al, 1975). The coexistence of uplifts/folds (positive anomalies) and synclines (negative anomalies) in Southeast Nigeria have been reported by previous studies (Benkhelil, 1987;Burke et al, 1970;etc).…”

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

“…Numerous studies have been conducted on developing various optimization algorithms, especially those based on natural phenomena, and their application to solve optimization problems in various fields of science and engineering (Nama et al., 2017). These algorithms have also been used to solve ill‐posed magnetic inverse problems, including Ant Colony Optimization (Liu et al., 2015; Srivastava et al., 2014), Barnacles Mating Optimization (Ai et al., 2022), Bat Optimization Algorithm (Essa & Diab, 2022), Differential Evolution (DE) (Balkaya et al., 2017; Du et al., 2021), Differential Search (Balkaya & Kaftan, 2021; Özyalın, 2023), Genetic Algorithm (Kaftan, 2017; Montesinos et al., 2016; Sohouli et al., 2022), Genetic‐Price Algorithm (GPA) (Di Maio et al., 2020), Gray Wolf Optimization (Agarwal et al., 2018), Hunger Games Search Algorithm (Ai et al., 2023), Manta Ray Foraging Optimization Algorithm (MRFO) (Ben, Ekwok, et al, 2022; Ben et al., 2021), Particle Swarm Optimization (PSO) (Ekinci et al., 2020; Ekwok et al., 2023; Liu et al., 2018; Srivastava & Agarwal, 2010), Social Spider Optimization (Ben, Akpan, et al., 2022), Whale Optimization Algorithm (WOA) (Divakar et al., 2018; Gobashy et al., 2020) and Simulated Annealing (SA) (Biswas et al., 2022; Biswas & Rao, 2021; Shinu & Dubey, 2023). The choice of the most appropriate algorithm for a given optimization problem may depend on several factors, such as the complexity of the problem, the size of the search space, the required precision, and the available computational resources (Dragoi & Dafinescu, 2021).…”