Novel technique for the interpretation of gravity anomalies over geologic structures with idealized geometries using the Manta ray foraging optimization

Ben, Ubong C.; Akpan, Anthony E.; Enyinyi, Eze Ohara; Awak, Emmanuel

doi:10.1016/j.jaesx.2021.100070

Cited by 14 publications

(7 citation statements)

References 87 publications

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“…Also, different optimization algorithms were applied to gravity modeling and interpretation such as genetic algorithm [ 32 ], bat algorithm [ 33 ], artificial bee colony (BCO) algorithm [ 34 ], cuckoo optimization algorithm [ 35 ], imperialist competitive algorithm [ 36 ], differential evolution algorithm [ 37 ], and Manta ray foraging optimization [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

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

Essa,

Abo-Ezz,

Géraud

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

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

Essa,

Abo-Ezz,

Géraud

et al. 2024

Heliyon

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“…The manta ray foraging optimization (MRFO) is a novel swarm algorithm developed by Zhao et al (2020) .The optimization process mimics the foraging behaviors of manta rays for the solution of physical problems. This is achieved through emulation of three of their most efficient foraging operators including chain foraging, cyclone foraging, and somersault foraging (Ben et al, 2021). Finally a Bat optimization algorithm is a recently developed metaheuristic algorithm was used in various geophysical applications to explore and explain the parameters of buried ore and mineral targets.…”

Section: Introductionmentioning

confidence: 99%

Tilt Angle Filter Effect on Noise Cancelation and Structural Edges Detection in Hydrocarbon Sources in a Gravitational Potential Field

Hosseini

Khah²,

Kianoush

et al. 2023

Preprint

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Noise cancelation is the process done to remove out-of-range anomalies and make better edge boundaries interpretation. One of the most challenging issues in describing gravitational maps is separating the anomalies related to shallow sources from the deep ones. Furthermore, Existing noise can make it arduous to separate shallow blurred boundaries. In this study in SE Iran, gravitational surveying was carried out in shallow areas from the west of Qeshm to the Hormuz islands in a regular network with a distance of one kilometer. The range of gravitational Bouguer was -297 to -330 mGal. Modeling and determining geometrical parameters revealed five negative anomalies from salt penetration. The residual gravity was computed by deducting gravitational effects related to deep sources from measured gravitational data. The hyperbolic tangent function was preferred by comparing the results and employing the tilt angle filter on different gravitational field maps using numerical coding. Correspondingly, estimating the boundary and edge of the subsurface masses will be better than local filters, and anomalies will be highlighted with more intensity. Furthermore, three major fault systems in the Zagros basin were determined as the primary origin of activity and expansion of Hormuz salt. Sensitivity analysis was employed utilizing analytical signals and maps of tilt angle filtering, which both revealed the same satisfying results of -297 to -330 mGal. In this article, the effect of the tilt angle local phase filter on a synthetic model was accomplished through numerical coding. Then the results were analyzed on the gravity data.

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“…Among existing meta-heuristics, the Manta Ray Foraging optimization algorithm is a recent Swarm Intelligence optimization algorithm developed by Zhao et al [2] in 2020. Due to its simplicity and easy implementation, it was widely applied for solving optimization problems such as electrical engineering [3], [4], image processing [5], [6], mathematics [7], geology [8], feature selection [9], system identification [10], energy [11]- [13], networking [14], PID control [15], and many others. Similar to other meta-heuristics, the MRFO algorithm suffers from premature and slow convergence and attempts to fall to local optima.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

et al. 2023

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The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.

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Novel technique for the interpretation of gravity anomalies over geologic structures with idealized geometries using the Manta ray foraging optimization

Cited by 14 publications

References 87 publications

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

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

Tilt Angle Filter Effect on Noise Cancelation and Structural Edges Detection in Hydrocarbon Sources in a Gravitational Potential Field

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

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