Evaluation of the parameters of the fault-like geologic structure from the gravity anomalies applying the particle swarm

Abstract

This study focuses on interpreting Bouguer gravity anomalies by two-sided fault structures. Faults have prime concerns for hazardous zones, mineralized areas, and hydrocarbon systems. The proposed scheme is done through the following steps: first, it utilizes the residual moving average anomalies estimated from the Bouguer gravity anomalies using several window lengths. Second, each residual anomaly is interpreted using the particle swarm. Third, calculate the average value for all interpreted anomalies. Fourth, the average values for the fault parameters are utilized to build the forward gravity model, which is compared with the true ones. The efficiency of this method has been studied by applying it to a synthetic example with different levels of impeded noise (0%, 5%, and 10%). Gravity data for fault structures were investigated from Egypt. It was found that the obtained results are in good agreement with the previously published studies.

Gravity profiles interpretation applying a metaheuristic particle optimization algorithm of mineralized bodies resembled by finite elements

“…Over the years, numerous geophysical methods for solving inverse problems have been developed, including robust simultaneous joint inversion [ 1 ], fair-function minimization [ 2 ], the s-curves method [ 3 ], non-linear least-squares [ 4 , 5 ], derivative-based approaches [ 6 , 7 ], moving average methods [ 8 , 9 ], multiple-linear regression [ 10 ], R-parameter imaging [ 11 ], and the Lanczos bidiagonalization method [ 12 ]. Metaheuristic optimization algorithms, such as genetic algorithms [ 13 ], particle swarm optimization [ [14] , [15] , [16] , [17] ], simulated annealing [ 18 ], cuckoo optimization algorithm [ 19 ], and ant colony algorithm [ 20 ], have also been used recently for gravity data inversion. These algorithms have shown promising results in optimizing non-linear and non-convex objective functions, and they can be applied to various geophysical problems like gravity, magnetic, and electrical data inversion [ [21] , [22] , [23] ].…”

Magnetic field analysis using the improved global particle swarm optimization algorithm to estimate the depth and approximate shape of the buried mass

“…This method as well as its improved variations have been used more in the fields of artificial intelligence and computer. In recent years, the PSO particle swarm optimization method has been used in various branches of geophysics, and researchers have devised various methods to improve the performance of this algorithm (Monteiro Santos, 2010;Toushmalani, 2013a and2013b;Pallero et al, 2015;Singh and Biswas, 2016;Singh and Singh, 2017;Essa and El-Hussein, 2017;Roshan and Singh, 2017;Essa andElhussein, 2018a, 2018b;Essa andMunschy, 2019, Eshaghzadeh andSahebari, 2020b;Essa 2021;Eshaghzadeh and Hajian, 2021).…”

Fast Inversion Approach for Modeling Gravity Data: Varying Synthetic and Real Cases

“…Nowadays, there are many methods have been developed, based on artificial intelligence (AI). As genetic algorithm (Montesinos, et al, 2005;and Di Maio, et al, 2020), the (DE) differential evolution algorithm (Ekinci, et al, 2016;and Balkaya, et al, 2017), the gravitational search algorithm (Rashedi, et al, 2009), the dolphin echolocation (Kaveh and Faroudi, 2013), the ant colony optimization (ACO) algorithm (Dorigo and Stützle 2003), the simulated annealing algorithm (SA) (Biswas, 2015), the particle swarm optimization (PSO) algorithm (Essa, 2021;Essa, et al, 2021b;and Essa, et al, 2022) Those algorithms are not unusualplace amongst researchers, due to their versatility and advanced cappotential to cope with a style of problems. This paper proposes a new approach to interpret the gravity data generated by faults.…”