Aeromagnetic and Radiometric Data Interpretation to Delineate the Structural Elements and Probable Precambrian Mineralization Zones: a Case Study, Egypt

Essa, Khalid S.; Munschy, Marc; Youssef, Mohamed Ahmed; Khalaf, Ezz El Din Abdel Hakim

doi:10.1007/s42461-022-00675-0

Cited by 17 publications

(13 citation statements)

References 59 publications

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“…Along with Eq. (11), the new offspring is formed by the female barnacle because the female barnacle retrieves the sperm from the water left by the other barnacles. As can be seen from the brief introduction to BMO, pl is the algorithm-based control parameter, which needs to be selected before employing BMO.…”

Section: Bmomentioning

confidence: 99%

“…The results have shown that the BMO algorithm can scan the model parameter space more extensively without affecting its ability to consistently approach the unique global minimum in this presented inverse problem. We, therefore, recommend the use of competitive BMO in model parameter estimation studies performed with other geophysical methods.The magnetic method has so far been used in a wide range of investigations at various sites [1][2][3][4][5][6][7][8][9][10][11] . The most important task of the magnetic method is to explore subsurface geology using minor changes in the geomagnetic field caused by magnetized masses.…”

mentioning

confidence: 99%

“…The magnetic method has so far been used in a wide range of investigations at various sites [1][2][3][4][5][6][7][8][9][10][11] . The most important task of the magnetic method is to explore subsurface geology using minor changes in the geomagnetic field caused by magnetized masses.…”

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confidence: 99%

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Magnetic anomaly inversion through the novel barnacles mating optimization algorithm

Essa

Ekinci

et al. 2022

Sci Rep

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Dealing with the ill-posed and non-unique nature of the non-linear geophysical inverse problem via local optimizers requires the use of some regularization methods, constraints, and prior information about the Earth's complex interior. Another difficulty is that the success of local search algorithms depends on a well-designed initial model located close to the parameter set providing the global minimum. On the other hand, global optimization and metaheuristic algorithms that have the ability to scan almost the entire model space do not need an assertive initial model. Thus, these approaches are increasingly incorporated into parameter estimation studies and are also gaining more popularity in the geophysical community. In this study we present the Barnacles Mating Optimizer (BMO), a recently proposed global optimizer motivated by the special mating behavior of barnacles, to interpret magnetic anomalies. This is the first example in the literature of BMO application to a geophysical inverse problem. After performing modal analyses and parameter tuning processes, BMO has been tested on simulated magnetic anomalies generated from hypothetical models and subsequently applied to three real anomalies that are chromite deposit, uranium deposit and Mesozoic dike. A second moving average (SMA) scheme to eliminate regional anomalies from observed anomalies has been examined and certified. Post-inversion uncertainty assessment analyses have been also implemented to understand the reliability of the solutions achieved. Moreover, BMO’s solutions for convergence rate, stability, robustness and accuracy have been compared with the solutions of the commonly used standard Particle Swarm Optimization (sPSO) algorithm. The results have shown that the BMO algorithm can scan the model parameter space more extensively without affecting its ability to consistently approach the unique global minimum in this presented inverse problem. We, therefore, recommend the use of competitive BMO in model parameter estimation studies performed with other geophysical methods.

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Section: Bmomentioning

confidence: 99%

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confidence: 99%

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Magnetic anomaly inversion through the novel barnacles mating optimization algorithm

Essa

Ekinci

et al. 2022

Sci Rep

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“…Compared with the conventional method, the predicted distribution of magnetization intensity obtained using train a DNN model was more concentrated and had a better resolution to determine the boundary of the magnetic body (Hu et al, 2021). The application of the total intensity magnetic and reduced-to-pole maps, power spectrum, analytic signal, tilt-angle, and local wavenumber maps were used to allocate and describe the structural elements and mineralization zones such as uranium, gold, and sulfide in recognizing magnetic sources distribution, lineament features, and mineral zones (Essa et al, 2022;Hosseini et al, 2023). Recently, Standard Particle Swarm Optimization (SPSO) and Genetic Algorithm (GA) have been commonly used in the geophysical inversion.…”

Section: Introductionmentioning

confidence: 99%

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

Kianoush

Khah²,

Hosseini

et al. 2023

Preprint

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Geophysical investigations are one of the leading efforts in any exploration project that must be performed ahead of time due to the high excavation expenditures, heavy machinery, and cost reduction. One of the essential geophysical concerns is the estimation of the physical and geometrical parameters of the reserve (geobody), which is done by exploiting the nonlinear inverse modeling of magnetic data. These parameters include depth, volume, shape factor, and type of storage. The present investigation aims to prepare and model magnetic data for proposed drilling sites in the Baba Ali Iron ore deposit in the Hamedan province of Iran. The areal and depth extent of the iron ore geobody was unknown. The Bhattacharyya method by MATLAB software coding was used to minimize the target function and potential field data and create the best fit for the magnetic data. The residual anomaly and model graphs exhibited an excellent two-dimensional conformation in this modeling. Also, 3D modeling correctly reconstructs the properties of the productive resources of anomalies. After preparing full magnetic maps, the magnetic lenses distinguished in four anomalies of surface depths, 20, 50, and deeper than 50 meters for this zone. This magnetite lens for the first zone was estimated based on filters applied on the entire magnetic map so that the lens's depth is trivial and almost zero. Their specific gravity is calculated as 4.77 t/m3, and their initial storage capacity is suggested to be about 95,400 tones at most. Finally, to complete the preliminary explorations of the specified area, exploratory drilling is suggested for three points by inverse modeling.

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“…Compared with the conventional method, the predicted distribution of magnetization intensity obtained using train a DNN model was more concentrated and had a better resolution to determine the boundary of the magnetic body (Ahumada et al, 2023;Hu et al, 2021). The application of the total intensity magnetic and reduced-to-pole maps, power spectrum, analytic signal, tilt-angle, and local wavenumber maps were used to allocate and describe the structural elements and mineralization zones such as uranium, gold, and sulfide in recognizing magnetic sources distribution, lineament features, and mineral zones (Essa et al, 2022;Hosseini et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

Kianoush

Khah²,

Hosseini

et al. 2023

Preprint

View full text Add to dashboard Cite

Geophysical investigations are one of the leading efforts in any exploration project that must be performed ahead of time due to the high excavation expenditures, heavy machinery, and cost reduction. One of the essential geophysical concerns is the estimation of the physical and geometrical parameters of the reserve (geobody), which is done by exploiting the nonlinear inverse modeling of magnetic data. These parameters include depth, volume, shape factor, and type of storage. The present investigation aims to prepare and model magnetic data for proposed drilling sites in the Baba Ali Iron ore deposit in the Hamedan province of Iran. The area is covered with 1000 points of geomagnetic reading with an almost 5*10 m regularly spaces grid trending WE. The areal and depth extent of the iron ore geobody was unknown. The Bhattacharyya method by MATLAB software coding was used to minimize the target function and potential field data and create the best fit for the measured and modelled magnetic data. The residual anomaly and model graphs exhibited an excellent two-dimensional conformation in this modeling. Also, 3D modeling correctly reconstructs the properties of the productive resources of anomalies. After preparing full magnetic maps, the magnetic lenses distinguished in four anomalies of surface depths, 20, 50, and deeper than 50 meters for this zone. This magnetite lens for the first zone was estimated based on analytical signal filters applied on the entire magnetic map so that the lens's depth is trivial and almost zero. Due to specific gravity calculated as 4.77 t/m3, initial storage capacity is suggested to be about 95,400 tons of magnetite, pyrite, and hematite minerals at most in an area about 6 Km2. Finally, to complete the preliminary explorations of the specified area, exploratory drilling is suggested for three points by inverse modeling.

show abstract

Aeromagnetic and Radiometric Data Interpretation to Delineate the Structural Elements and Probable Precambrian Mineralization Zones: a Case Study, Egypt

Cited by 17 publications

References 59 publications

Magnetic anomaly inversion through the novel barnacles mating optimization algorithm

Magnetic anomaly inversion through the novel barnacles mating optimization algorithm

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

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