Browsing by Author "Baddari, K."

Now showing 1 - 14 of 14

Acoustic impedance inversion by feedback artificial neural network
(Elsevier, 2010) Baddari, K.; Djarfour, Noureddine; Aïfa, Tahar; Ferahtia, J.
The determination of acoustic impedance distribution from the seismic data field measurement can be expressed as an ill-posed inverse problem. This work deals with the use of the Elman artificial neural network (ANN) (feedback connection) for the seismic data inversion. In the proposed structure the hidden neuron outputs from the previous time step are fed back to their inputs through time delay units; this enables them to process temporal behaviour and provide multi-step-ahead predictions. The ANN architectures and learning rules are presented to allow the best estimate of acoustic impedance from seismic data. The effects of network architectures using 5 to 60 neurons and 10 to 90 neurons in the hidden layer respectively for synthetic and real data on the rate of convergence and prediction accuracy of ANN models are discussed. The behaviour of networks observed on training data is very similar to the one observed on test data. The results obtained clearly prove the feasibility of the proposed method for seismic data inversion by feedback neural networks. Different tests indicate that the back-propagation conjugate gradient algorithm can easily train the proposed Elman ANN structure without getting stuck in local minima
Adaptive Clutter-Map CFAR detection in distributed sensor networks
(Elsevier, 2016) Bouchelaghem, H.E.; Hamadouche, M.; Soltani, F.; Baddari, K.
In this paper, the problem of Clutter Map Constant False Alarm Rate (CMAP-CFAR) detection is considered for a distributed detection with two and three sensors and a data fusion centre. We assume that the sensors are identical and that the target is a fluctuating according to the Swerling I model, embedded in a white Gaussian noise of unknown variance. Closed form expressions of the global probabilities of detection and false alarm for the ‘AND’, ‘OR’ and ‘MAJORITY’ rules and the Adaptive Detection Threshold are determined and the performance of the system is investigated and analyzed
Application of feedback connection artificial neural network to seismic data filtering
(Elsevier, 2008) Djarfour, Noureddine; Aïfa, Tahar; Baddari, K.; Mihoubi, A.; Ferahtia, J.
The Elman artificial neural network (ANN) (feedback connection) was used for seismic data filtering. The recurrent connection that characterizes this network offers the advantage of storing values from the previous time step to be used in the current time step. The proposed structure has the advantage of training simplicity by a back-propagation algorithm (steepest descent). Several trials were addressed on synthetic (with 10% and 50% of random and Gaussian noise) and real seismic data using respectively 10 to 30 neurons and a minimum of 60 neurons in the hidden layer. Both an iteration number up to 4000 and arrest criteria were used to obtain satisfactory performances. Application of such networks on real data shows that the filtered seismic section was efficient. Adequate cross-validation test is done to ensure the performance of network on new data sets
Application of signal dependent rank-order mean filter to the removal of noise spikes from 2D electrical resistivity imaging data
(2009) Ferahtia, J.; Djarfour, Noureddine; Baddari, K.; Guérin, R.
It is well-known that when inverting two-dimensional (2D) electrical resistivity data, a major source of errors is the presence of noise and in particular noise spikes. The popular median filter is often applied to the removal of single spikes. However, when the signal is highly corrupted with successive spikes, the median filter performance is poor. This paper deals with the use of the signal dependent rank-order mean filter for the detection and removal of noise spikes from highly corrupted 2D electrical resistivity imaging data. In addition to its computational simplicity, this filter is shown to be extremely robust, even in the presence of very strong noise, especially when it is applied recursively. The signal dependent rank-order mean filter was tested on 2D synthetic resistivity data contaminated by near-surface inhomogeneities and the results confirmed efficient removal of the disturbances normally associated with near-surface inhomogeneities. The signal dependent rank-order mean filter was also applied to field data and demonstrated its ability to significantly improve the accuracy of the inversion process and to produce good visual results in the inverted electrical sections
Apport de la géoélectricité et des diagraphies à la reconnaissance des minéralisations polymétalliques dans la région d'El Tarf - prospect de Zitouna
(2010) Medkour, M.; Baddari, K.; Chaab, S.; Laouar, A.
Les cibles courantes en méthodes électriques et en polarisation provoquée sont les gisements de minerais métalliques, qu’ils soient massifs ou disséminés. Ces cibles sont généralement conductrices et ont des propriétés physiques caractéristiques permettant de les détecter dans les conditions in situ à l’aide de mesures faites en surface. A ce propos, L’analyse et l’interprétation des documents graphiques élaborés à partir des données de base acquises par l’O.R.G.M au cours de la campagne de prospection géophysique de détail de 1999-2001 sur les secteurs limitrophes de Djebel Zitouna et Kef Zoukrane a permis de relever quelques anomalies de polarisation provoquée appréciables en extension et en amplitude. Ces anomalies semblent traduire l’effet des zones minéralisées en pyrite, galène et sphalérite. Les corps perturbateurs ayant généré les anomalies observées peuvent se définir de par leurs caractères géoélectriques comme des corps résistants chargeables. Les forages implantés à posteriori aux épicentres des anomalies PP ont recoupé des niveaux minéralisés dans les grès numidiens fissurés et bréchifiés, confortant ainsi les interprétations antérieures
Caractérisation de la densité de charge de surface de membranes nanoporeuse
(Université M'Hamed Bougara Boumerdes, 2013) Hanafi, Yamina; Baddari, K.; Szymczyk, A.; Zibouche, F.
La sélectivité d’une membrane vis-à-vis d’un soluté dépend en partie de sa structure poreuse (effets stériques) mais il est désormais clairement établi qu’une part non négligeable de la sélectivité est régie par les interactions électrostatiques entre les espèces chargées présentes en solution et la surface de la membrane elle-même chargée dans la plupart des cas. Dès lors, la détermination de paramètres représentatifs des interactions membrane-solution, tels que le potentiel zêta, constitue un atout majeur pour la compréhension et la prévision des performances de filtration d’une membrane. Il s’avère donc essentiel, pour optimiser les conditions d’utilisation d’une membrane, d’étudier préalablement ses propriétés électriques et électrocinétiques. Celle-ci consiste à mesurer le potentiel et le courant d’écoulement, peuvent ensuite être reliés directement à la densité de charge de surface du matériau étudié. Une caractérisation préliminaire réalisée avec des films denses de Téflon nous a permis de valider la méthode de mesure du courant d’écoulement. Cette étape était nécessaire car la mesure du courant d’écoulement est délicate à mettre en œuvre puisque les courants mesurés n’excèdent pas quelques dizaines de nanoampères. Les résultats obtenus avec une membrane polyamide NF270 ont permis de mettre en évidence la présence d’une charge de surface négative (due à la présence de fonctions acides carboxyliques) et de comprendre les écarts de perméabilités de différents types d’électrolytes à travers cette membrane
Complex-valued forecasting of the global solar irradiation
(2013) Saoud, L. Saad; Rahmoune, F.; Tourtchine, V.; Baddari, K.
In this paper, a forecasting of the global solar irradiation in the complex-valued domain is proposed. A method to transform the meteorological data into complex values is developed and the Complex Valued Neural Network (CVNN) is used to model and forecast the daily and the hourly solar irradiation. The measured data of Tamanrasset city, Algeria (altitude: 1362 m; latitude: 22°48 N; longitude: 05°26 E) is used to validate the developed model. In the hourly solar irradiation case, the 24 h ahead will be forecasted using the combination of the past daily meteorological dataset. Several models are presented to test the feasibility and the performance of the CVNN for forecasting either daily or hourly solar irradiation for both multi input single output and multi input multi output strategies. Results obtained throughout this paper show that the CVNN technique is suitable for modeling and forecasting daily and hourly solar irradiation
Equations de la physique mathématique appliquées
(Office des Publications Universitaires, 2009) Baddari, K.; Abbassov, A.
A fuzzy logic-based filter for the removal of spike noise from 2D electrical resistivity data
(Elsevier, 2012) Ferahtia, J.; Djarfour, Noureddine; Baddari, K.; Kheldoun, Aissa
In this paper, a filter based on fuzzy logic is proposed to remove spike noise from 2 dimensional electrical resistivity data. The noise detection used in this paper is based on differentiating noisy samples from the central sample inside a moving window. These fuzzy derivatives are used by the fuzzy inference system to detect corrupted samples. To assess the performance of the proposed filter for the removal of spike noise, the root-mean squared error as well as the signal-to-noise ratio were used as an objective criterion. It has been demonstrated by synthetic and real examples that the proposed filter achieves quite good results compared to the standard median filter as well as to the very effective SD-ROM filter
Image-based processing techniques applied to seismic data filtering
(Elsevier, 2013) Ferahtia, J.; Aïfa, Tahar; Baddari, K.; Djarfour, Noureddine; Eladj, S.
Impact of integrated clean energy on the future of the mediterranean: Exploitation of albian geothermal water in South Algeria
(Elsevier Ltd, 2011) Ouali, S.; Benaïssa, Z.; Belhamel, M.; Khellaf, A.; Baddari, K.; Djeddi, M.
The Intercalary Continental aquifer generally called Albian aquifer constitutes the main geothermal resource in South Algeria. Additionally it represents the biggest water reserve in the word. Albian aquifer is used since centuries, especially in the areas where it levels like Tidikelt Touat and Gourara. But however in other areas where this aquifer is deep and whose water is hot, the exploitation of the aquifer is rather difficult. As the only geothermal resource in southern Algeria, the Albian aquifer has a lot of interest from geothermal point of view; therefore good knowledge of the different routes of its exploitations is necessary to facilitate future applications of geothermal in the Saharan regions. The present article gives an overview about various modes of exploitation of the Intercalary Continental aquifer and the major problems which have occurred during exploitation. Finally is added discussion about the main applications of geothermal energy in South Algeria based on projects completed or in progress
Physique de la terre
(OPU, 2009) Baddari, K.; Djeddi, M.
Ce livre s'adresse aux étudiants, chercheurs et les enseignants physiciens, mathématiciens, géophysiciens et aux spécialistes des sciences de la terre
Probabilistic seismic hazard assessment in the northeastern part of Algeria
(Springer, 2017) Hamlaoui, Mahmoud; Vanneste, K.; Baddari, K.; Louail, L.; Vleminckx, B.; Demdoum, A.
Tomographic velocity images by artificial neural networks
(2007) Djarfour, Noureddine; Ferahtia, J.; Baddari, K.
The present study deals with the use of Elman artificial neural network (feedback connexion) to reconstruct the velocity image from a traveltime in the seismic tomography experiment. This recurrent connection provides the advantage to store values from the previous time step, which can be used in the actual time step. The backpropagation algorithm has been used to learn the suggested neural network. Efficiency of these networks has been tested in training and generalization phases. A comparative reconstruction with two classical methods was performed using backprojection and Algebraic Reconstruction Techniques (ART). The obtained results clearly show improvements of the quality of the reconstruction obtained by artificial neural networks