Comparative depth estimates and modeling of magnetic anomalies over the Nkalagu area, Southeastern Nigeria

Obiora, Daniel N.; Oha, I. A.; Ihedike, Adolphus O.; Igwe, Emmanuel

doi:10.1007/s40808-021-01155-y

Cited by 5 publications

(6 citation statements)

References 23 publications

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“…Structural trends within the study area as shown in the residual map are mainly NE-SW and NW-SE directions are in agreement with the fault orientation within the Benue Trough, with the NE-SW trends being dominant. The result from this study is in agreement with most of the depth estimation studies within the study area [1,14,[23][24][25][30][31]. The results obtained from spectral analysis for the depth to magnetic sources vary between 2.585 km to 4.878 km for deep structures, with a mean value of 3.415km and 0.135 km to 0.200 km for the shallow structures, with a mean value of 0.158 km.…”

Section: Discussionsupporting

confidence: 91%

“…Estimation of relief on the basement surface may be related to structures that harbour hydrocarbon in overlying sedimentary basins [8]. Several studies have been carried out for the estimation of depth to the basement using the spectral inversion method and 3D Euler Deconvolution [8,13,15,[23][24][30][31][32]44,47,49,50]. Ofoegbu [51] estimated the thickness of the sediments in the Lower and Middle Benue Trough to vary between 0.5 and 7.0 km.…”

Section: Introductionmentioning

confidence: 99%

“…93 km to 5790.49 km respectively. Obiora et al [23] using Standard Euler deconvolution for the structural index (SI) of 0.5, 1.0, 2.0 and 3.0, observed depths to magnetic sources ranging from 0 to 1500 m, 300 to 3300 m, 0 to 4000 m and 200 to 4200 m, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Basement depth re-valuation of anomalous magnetic bodies in the lower and middle Benue trough using Euler deconvolution and spectral inversion techniques

Omenikolo¹,

Emberga²,

Opara³

2022

World J. Adv. Res. Rev.

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Spectral technique and Euler Deconvolution were applied to high-resolution aeromagnetic data of parts of Benue trough to estimate the depth of anomalous magnetic sources within the study area; Data enhancement techniques such as total magnetic intensity map, reduction to pole, regional-residual separation and upward continuation maps were employed to identify different magnetic anomalies, structural trends representing the tectonics of the location were observed trending NE-SW and N-S directions; The result of 3D Euler deconvolution for the structural index (SI) = 0, 1, 2, 3 gave depths to magnetic sources that range from -589;3 m to -2678;8 m, -459;0 m to -2691;9 m, -294;6 m to -2817;5 m, - 430;2 m to -2780;6 m respectively; The depth estimates from 2-D spectral revealed a two-layer model; The shallow magnetic depth ranges between 0;135 km to 0;200 km with a mean depth of 0;158 km and the depth to magnetic basement vary between 2;585 km to 4;878 km with a mean depth of 3;415 km; This result, therefore, indicates that the average basement depth of the study area obtained from the spectral analysis is 3;415km; This investigation, therefore provides appropriate sedimentary thickness for suitable hydrocarbon prospecting within the study area.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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Basement depth re-valuation of anomalous magnetic bodies in the lower and middle Benue trough using Euler deconvolution and spectral inversion techniques

Omenikolo¹,

Emberga²,

Opara³

2022

World J. Adv. Res. Rev.

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“…infiltrates the geomagnetic field, the target is magnetized by the geomagnetic field, and a secondary field is formed in the space of the original magnetic field, distorting the surrounding magnetic field [ 3 ]. Detecting the magnetic field in space to identify subsurface [ 4 , 5 ] or underwater metal targets [ 6 ] has several applications in the mine detection field [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…There are many technical techniques for detecting metal targets, with magnetic detection being one of the most prevalent. Detecting the total magnetic field, the vector magnetic field, the gradient magnetic field, and the gradient tensor magnetic field are all popular methods for measuring magnetic fields in space [ 7 ]. Different techniques for detecting the space magnetic field result in varying numbers of sensors, and sensors with a sensible arrangement collect more information about the space magnetic field [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure

Han

Zhang

et al. 2022

Sensors

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After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy compared with the traditional recognition algorithm for measuring the magnetic moment of the target and the included geomagnetism angle. In this paper, a ResNet-18-based recognition system is presented for classifying metallic object types. First, a fluxgate magnetometer cube arrangement structure (FMCAS) magnetic field feature collector is constructed, utilizing an eight-fluxgate magnetometer sensor array structure that provides a 400 mm separation between each sensitive unit. Magnetic field data are acquired, along an east–west survey line on the northern side of the measured target using the FMCAS. Next, the location and type of targets are modified to create a database of magnetic target models, increasing the diversity of the training dataset. The experimental dataset is constructed by constructing the magnetic flux density tensor matrix. Finally, the enhanced ResNet-18 is used to train the data for the classification recognition recognizer. According to the test findings of 107 validation set groups, this method’s recognition accuracy is 84.1 percent. With a recognition accuracy rate of 96.3 percent, a recall rate of 96.4 percent, and a precision rate of 96.4 percent, the target with the largest magnetic moment has the best recognition impact. Experimental findings demonstrate that our enhanced RestNet-18 network can efficiently classify metallic items. This provides a new idea for underground metal target identification and classification.

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Tectonic elements and structural framework deduced from magnetic data of the Southern Desert, Iraq

Al-Bahadily,

Al-Rahim,

Smith

2024

Pure Appl. Geophys.

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Comparative depth estimates and modeling of magnetic anomalies over the Nkalagu area, Southeastern Nigeria

Cited by 5 publications

References 23 publications

Basement depth re-valuation of anomalous magnetic bodies in the lower and middle Benue trough using Euler deconvolution and spectral inversion techniques

Basement depth re-valuation of anomalous magnetic bodies in the lower and middle Benue trough using Euler deconvolution and spectral inversion techniques

Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure

Tectonic elements and structural framework deduced from magnetic data of the Southern Desert, Iraq

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