Identification of deep magnetized structures in the tectonically active Chlef area (Algeria) from aeromagnetic data using wavelet and ridgelet transforms

Boukerbout, Hassina; Abtout, Abdeslam; Gibert, Dominique; Bernard, Henry; Bouyahiaoui, Boualem; Derder, M. E. M.

doi:10.1016/j.jappgeo.2018.04.026

Cited by 11 publications

(8 citation statements)

References 78 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A good correlation is shown with N-S geological profile (Figure 5). Aeromagnetic map of Chlef region: total field anomaly reduced to the pole (Modified from [31]).…”

Section: Application To Aeromagnetic Datamentioning

confidence: 99%

“…This method utilizes the homogeneity properties of the potential field, to identify and localize the causative sources [22][23][24][25]. Further works show the robustness of this method with respect to noise [26,27], as revealed by many applications in geophysical prospecting, such as in aeromagnetics data [28][29][30][31], spontaneous electrical potential [32][33][34]; gravity data [35][36][37] and electromagnetic data [38]. The 2D wavelet method is then developed [30,39] in order to localize and identify the potential fields anomalies causative structures in the case of elongated structures such as dykes, faults, etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Use of the 2D and 3D Complex Wavelet and Ridgelet Transforms in Geophysical Prospecting: Case of Potential Fields Data

Boukerbout

2024

Modern Applications of Wavelet Transform

View full text Add to dashboard Cite

The complex wavelet and ridgelet transforms are used in the potential field data interpretation for identifying the buried structures responsible for potential field anomalies. Its basis is the use of particular analyzing wavelets belonging to the Poisson semigroup that possess amazing properties regarding potential fields. In fact, the analyzed anomaly displays a conical signature in the wavelet domain and whose apex is pointing out at the causative homogeneous structure. Fundamentally, the interpretation is performed in the upward-continued domain where, the dilation of the wavelet transform is the upward-continuation altitude. This confers on the wavelet transform a considerable advantage: its robustness with respect to noise. The method is also developed to identify the depth, horizontal positions, size, strike direction, dips and shape of elongated 3D structures such as finite-dimensional dykes and faults. For this type of anomaly, the 2D wavelet transform corresponds to the ridgelet transform performed in the Radon domain, where elongated anomalies are recognized by high amplitude signatures. A reminder of the developed theory and applications in the 2D and 3D cases on real case studies are shown.

show abstract

“…A good correlation is shown with N-S geological profile (Figure 5). Aeromagnetic map of Chlef region: total field anomaly reduced to the pole (Modified from [31]).…”

Section: Application To Aeromagnetic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Use of the 2D and 3D Complex Wavelet and Ridgelet Transforms in Geophysical Prospecting: Case of Potential Fields Data

Boukerbout

2024

Modern Applications of Wavelet Transform

View full text Add to dashboard Cite

show abstract

“…In order to avoid the disadvantages of the wavelet neural network, the prediction model should be improved. The ridgelet transform not only has the scale and location channels like wavelet but also has direction; therefore the ridgelet transform has good performance of processing the linear and hyper plane singularity. Therefore, the ridgelet function is used to replace the wavelet function to construct the ridgelet neural network with compact structure and good generalization performance, and then the prediction precision of model can be improved.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Zhao

Ren

Gao

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.

show abstract

“…Geomagnetic method is an effective tool in investigating magnetized geologic sources (Boukerbout et al., 2018; Ekinci, Büyüksaraç, et al., 2020; Ekinci & Yiğitbaş, 2012; Ekvok et al., 2022; Essa, Munschy, et al., 2022; Kaftan, 2017; Oruç, 2011; Pham et al., 2022; Xu et al., 2011). Additionally, it is frequently applied in economic resource explorations due to the adequate magnetization contrast between surrounding rocks and ore/mineral deposits (Bencharef et al., 2022; Biswas, 2018; Eldosouky et al., 2021; Hinze et al., 2013; Kharbish et al., 2022; Spector & Lawler, 1995).…”

Section: Introductionmentioning

confidence: 99%

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

View full text Add to dashboard Cite

Geomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

show abstract

Identification of deep magnetized structures in the tectonically active Chlef area (Algeria) from aeromagnetic data using wavelet and ridgelet transforms

Cited by 11 publications

References 78 publications

The Use of the 2D and 3D Complex Wavelet and Ridgelet Transforms in Geophysical Prospecting: Case of Potential Fields Data

The Use of the 2D and 3D Complex Wavelet and Ridgelet Transforms in Geophysical Prospecting: Case of Potential Fields Data

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Contact Info

Product

Resources

About