Browsing by Author "Ferahtia, Jalal"

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    Application of a radial basis function artificial neural network to seismic data inversion
    (Elsevier, 2009) Baddari, Kamel; Aïfa, Tahar; Djarfour, Noureddine; Ferahtia, Jalal
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    Incorporation of a non-linear image filtering technique for noise reduction in seismic data
    (Springer, 2010) Ferahtia, Jalal; Baddari, Kamel; Djarfour, Noureddine; Kassouri, Abdel Kader
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    Seismic noise attenuation by means of an anisotropic non-linear diffusion filter
    (Elsevier, 2011) Baddari, Kamel; Ferahtia, Jalal; Aïfa, Tahar; Djarfour, Noureddine
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    Seismic noise filtering based on Generalized Regression Neural Networks
    (Elsevier, 2015) Djarfour, Noureddine; Ferahtia, Jalal; Babaia, Foudel; Baddari, Kamel; Said, El-adj
    This paper deals with the application of Generalized Regression Neural Networks to the seismic data filtering. The proposed system is a class of neural networks widely used for the continuous function mapping. They are based on the well known nonparametric kernel statistical estimators. The main advantages of this neural network include adaptability, simplicity and rapid training. Several synthetic tests are performed in order to highlight the merit of the proposed topology of neural network. In this work, the filtering strategy has been applied to remove random noises as well as source-related noises from real seismic data extracted from a field in the South of Algeria. The obtained results are very promising and indicate the high performance of the proposed filter in comparison to the well known frequency–wavenumber filter
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    Traitement de données géophysiques à base de filtres non- linéaires et adaptatifs : application des filtres : de diffusion anisotropique, trilatéral et SD-ROM à l’atténuation des bruits cohérents et aléatoires
    (2009) Ferahtia, Jalal
    L'évolution des technique d'acquisition et les nouvelle exigence d'interprétation engendré de nouvelle problématique au niveau des méthode de filtrage en géophysique. La perpétuelle quête de moyens permettant d'améliorer la qualité du signal, tout en préservant l'amplitude, est devenue un impératif, voir même en norme. La plupart des techniques de filtrage linéaire utilisées supposent que l'information a priori sur le signale et le bruit est connue.Or, souvent cette in formation fait défaut. L’utilisation de critères physiques pour séparer le bruit du signal est l'autre handicap de ces technique, en particulier lorsque ceux-ci sont fortement lié ou possèdent des spectres qui se superposes. Cette thèse vise à apporter des améliorations aux technique de filtrage non linéaire et adaptatif appliqué à la géophysique, en s'appuyant sur trois filtre inspirés du domaine du traitement de l'image. Trois principaux filtres ont été retenus à savoir le filtre de diffusion anisotropique, le filtre trilatérale et le filtre SD-ROM. Ces filtre connaissent un plein essor depuis une dizaine d'années dans le domaine de l’imagerie. Malheureusement, peu ou pas d'étude, ont été dédiées à ces filtre dans le domaine du traitement des données géophysique
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    Vertical electrical sounding data inversion using continuous ant colony optimization algorithm : a case study from Hassi R'Mel, Algeria
    (John Wiley and Sons Inc, 2022) Bouchaoui, Lyes; Ferahtia, Jalal; Farfour, Mohammed; Djarfour, Nouredine
    Among the existing geophysical methods, the vertical electrical sounding remains a fast and economical way to detect groundwater resources. However, the interpretation of the vertical electrical sounding data often suffers from non-uniqueness due to the ill-posed nature of the inverse problem. In recent years, metaheuristic algorithms have been successfully used for solving ill-conditioned and ill-posed problems. This work presents a scheme that uses the continuous ant colony optimization (ACOR) technique to invert vertical electrical sounding data. The ACOR is a global search algorithm that explores and finds the globally optimal solution over a search space by mimicking the behaviour of biological ants. The development of this algorithm was due to the requirement to interpret a set of vertical electrical sounding collected at the region of Hassi R'Mel (Algerian Sahara). The area has a particular geological/geoelectrical structure, which renders the interpretation of vertical electrical sounding challenging as standard inversion approaches tend to fail to recover a reliable resistivity model. The ACOR algorithm was initially tested with synthetic data from models simulating the geological/hydrogeological structure of the studied area. The results verified the robustness and stability of the ACOR algorithm even in the presence of a high level of noise. Furthermore, the tests indicated that the ACOR algorithm performed better when compared to other inversion techniques for this particular geoelectrical structure. Five vertical electrical sounding profiles using a Schlumberger array collected in the region of Hassi R'Mel were inverted using the ACOR algorithm. The models confirmed the presence of the two central aquifer systems and showed the geometry of the aquifer with the most favourable conditions for water accumulations
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    Vertical electrical sounding data inversion using continuous ant colony optimization algorithm: A case study from Hassi R’Mel, Algeria
    (2022) Bouchaoui, Lyes; Ferahtia, Jalal; Farfour, Mohammed; Djarfour, Nouredine
    Among the existing geophysical methods, the vertical electrical sounding remains a fast and economical way to detect groundwater resources. However, the interpretation of the vertical electrical sounding data often suffers from non-uniqueness due to the ill-posed nature of the inverse problem. In recent years, metaheuristic algorithms have been successfully used for solving ill-conditioned and ill-posed problems. This work presents a scheme that uses the continuous ant colony optimization (ACOR) technique to invert vertical electrical sounding data. The ACOR is a global search algorithm that explores and finds the globally optimal solution over a search space by mimicking the behaviour of biological ants. The development of this algorithm was due to the requirement to interpret a set of vertical electrical sounding collected at the region of Hassi R’Mel (Algerian Sahara). The area has a particular eological/geoelectrical structure, which renders the interpretation of vertical electrical sounding challenging as standard inversion approaches tend to fail to recover a reliable resistivity model. The ACOR algorithm was initially tested with synthetic data from models simulating the geological/hydrogeological structure of the studied area. The results verified the robustness and stability of the ACOR algorithm even in the presence of a high level of noise. Furthermore, the tests indicated that the ACOR algorithm performed better when compared to other inversion techniques for this particular geoelectrical structure. Five vertical electrical sounding profiles using a Schlumberger array collected in the region of Hassi R’Mel were inverted using the ACOR algorithm. The models confirmed the presence of the two central aquifer systems and showed the geometry of the aquifer with the most favourable conditions for water accumulations.