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Regional magnetic data in the southeastern segment of the Nigerian Niger Delta were evaluated with the aim of mapping deep-seated tectonic elements. Enhanced filtering operations and 3D forward modelling were applied on the magnetic data. These geologic features triggered the formation of rollover anticlines and faults that serve as structural traps in the study area. The filtered residual magnetic data revealed geologic structures characterized with NE - SW, N - S, and E - W orientations. The 3-D models detected the faulted crustal blocks, gradient zones, and intra-basement compositional magnetic variations. Furthermore, some prominent horst and graben structures as well as related normal faults characterized with distinct magnetic signatures were observed. Faults of base magnetic (of various compositions) were observed to be the fabricating mechanisms of the magnetic anomalies. Collectively, these structures influenced the patterns of magnetic anomalies with direct effects on the hydrocarbon trapping systems, as well as the pathways and accumulation zones for hydrothermal minerals. On the whole, the interpreted results revealed that the basement surface is rippling. Additionally, the depth result showed sedimentary thicknesses that ranged from 4–10 km. Again, the estimated crustal thickness varied from 14 to 19 km. This study has displayed the capabilities of the magnetic method in mapping the depth and configuration of basement rocks, which are crucial in controlling the formation of structural traps. Identifying these basement structures early helps in understanding the overall geological framework and potential hydrocarbon systems.

The Abakaliki Anticlinorium and its adjoining areas were appraised with the object of delineating high geothermal potential zones. Spectral depth analysis involving an improved centroid technique was used to analyze high-quality magnetic data. The obtained geothermal parameters were gridded to map various geothermal features within the investigated area. The obtained results varied from 4.99–9.35 km, 2.31–6.15 km, 6.11–16.28 km, 35.63°C–94.93°C/km, and 89.07–237.32 mW/m2 for centroid depth, top depth, Curie point depth, geothermal gradient, and heat flow values, respectively. The delineated semioval structure in the central zone of the investigated region characterized by a shallow Curie point depth (< 8.5 km) correlates with the location of the high-heat flow (>191.0 mW/m2) and geothermal gradient (>74.0°C/km) region. The high geothermal potential of the region is triggered by the massive post-rift tectonic event of the Santonian period related to the Abakaliki Anticlinorium. Further geophysical exploration programs should be carried out before exploitation activities at anomalous geothermal regions.

Litho-facies classification is an essential task in characterizing the complex reservoirs in petroleum exploration and subsequent field development. The lithofacies classification at borehole locations is detailed but lacks in providing larger coverage areas. The acquired 3D seismic data provides global coverage for studying the reservoir facies heterogeneities in the study area. This study applies six supervised machine learning techniques (Random Forest, Support Vector Machine, Artificial Neural Network, Adaptive Boosting, Xtreme Gradient Boosting, and Multilayer Perceptron) to 3D post-stack seismic data to accurately estimate different litho-facies in inter-well regions and compares their performance. Initially, the efficacy of the said models was critically examined via the confusion matrix (accuracy and misclass) and evaluation matrix (precision, recall, F1-score) on the test data. It was found that all the machine learning models performed best in classifying the shale facies (87%–94%) followed by the sand (65%–79%) and carbonate facies (60%–78%) in the Penobscot field, Scotian Basin. On an overall accuracy scale, we found the multilayer perceptron method the best-performing tool, whereas the adaptive boosting method was the least-performing tool in classifying all three litho-facies in the current analysis. While other methods also performed moderately good for the classification of all three litho-facies. The predicted litho-facies using seismic attributes matched well with the log data interpreted facies on the borehole locations. It indicates that the facies estimated in inter-well regions are accurate and reliable. Furthermore, we validated the estimated results with the other seismic attributes to ascertain the accuracy and reliability of the predicted litho-facies between the borehole locations. This study recommends machine learning applications for litho-facies classification to reduce the risk associated with reservoir characterization.

The southwest part of Cameroon is composed of a range of geological structures and sedimentary basins, whose geological history dates from the Cretaceous, and their establishment would have originated from the opening of the South Atlantic. Among these structures, the most important is the Cameroon Volcanic Line, generally denoted CVL, followed by the southern part of the Central African Shear Zone (CASZ) called the Foumban Shear Zone (FSZ), the Ntem Complex, the Benue Trough, the northern margin of the Congo Craton, and many others. The major structures identified in this part are formed as a result of geodynamic activity between the Oligocene and the recent period, to which we can add the volcanism that exists along with the continental limit. The XGM201e_2159 model is used to highlight the geological structures of Southwestern Cameroon. First, we separated the regional and residual anomalies from Bouguer gravity data. Then, we used a combination of filters to enhance the signature of the residual anomalies in Southwest Cameroon. These filters identified various geological structures in the area. Finally, we applied the enhanced horizontal gradient amplitude (EHGA) and multi-scale horizontal derivative of the vertical derivative (MSHDVD) methods to reveal the geological contacts, allowing us to establish the corresponding structural map of the region. In addition, the results obtained in this study are the first to precisely define the circumferential demarcation of the continental and oceanic expansions of Mount Cameroon, while clearly illustrating the Bao, Bomana, Tiko, and Ekona faults that extend to the Bakassi Peninsula and the Douala outlet. Furthermore, they highlight the strike–slip faults in the summit vicinity. The structural map shows that most of the geological boundaries identified in the area are trending in the NE–SW, NNE–SSW, ENE–WSW, N–S, and NW–SE directions.

The Manta-Ray Foraging Optimization has been adapted and implemented in computing model parameters from potential field anomalies originating from two-dimensional dipping faults. The inversion technique was originally demonstrated on magnetic anomalies from uncorrupted–then, corrupted synthetic datasets. Thereafter, it was experimented on profiles taken from mining fields in the United States, and Australia. The results obtained showed that the design procedure is admirably stable and flexible, especially when dealing with noisy data. It is also notably efficient in the quantitative resolution of geophysical inverse problems. The consistency in results obtained from analysis of deep-seated and shallow field examples, even when compared against background results, is also impressive. The new technique has also exhibited notable superiorities over other well-known and conventional techniques, especially on the grounds of convergence rate, cost, and quality of resolved anomaly parameters. Consequently, it is recommended for interpretation of other structures and modeling of other geophysical data like self-potential and resistivity data.

In this paper, a geophysical strategy based on the recently proposed Manta-Ray Foraging (MRF) Optimization algorithm is adapted and presented for the blind computation of depth/shape defining parameters from magnetic anomalies due to buried geo-bodies. The model parameters deciphered are the coefficient of amplitude (K), buried structure’s origin (x0), the depth (z), magnetization angle (α), and a shape factor (q). After detailed and piecewise design, the new inversion tool is originally trial-tested on anomaly data generated synthetically. The uncorrupted version of the test data is first analyzed, then - it is corrupted with noise varied at 5, 10, 15, and 20% corruption levels. Thereafter, it is experimented with magnetic profiles taken from exploration fields in the United States, Peru, and Egypt. From the evaluation of results obtained, the new procedure is observed as exhibiting outstanding stability and flexibility especially with noisy dataset and notable efficiency in the quantitative resolution of magnetic inversion problems. The results obtained for the field cases are also mostly consistent especially when compared with background results from similar studies conducted with other methods; further affirming the new tool as reliable for the geophysical investigation of buried minerals.