PRG 2013 Abstracts


Full Papers
Paper Nr: 1
Title:

Effective Residual and Regional Gravity Anomaly Separation - Using 1-D & 2-D Stationary Wavelet Transform

Authors:

Naeim Mousavi, Vahid E. Ardestani and Hassan Moosavi

Abstract: Numerous studies on capabilities of de-noising and separation by wavelet were performed, and their all aims more and less was elimination of possible largest nongeological factors, noise, and to achieve pure regional effects free from residuals. De-noising could be used for removal of non-desired effects like latitude, terrain, tides, drift etc., from our desired portion of data as target. Separations of anomalies that are not of interest conclude shallow structure is suitable to be optimal. Hence detection and removal of ever larger surface anomalies to obtain optimal separation is of interest. At up to now studies, large deviation of primarily original signal has been prevented. In this paper controlling factors which limit the overall deviation of transformed signal from the original one have been replaced with two new parameters that simultaneously cause extracting the maximum surplus signals, residuals, and also preserving the original form ever possible. Results of artificial models along with application of separation to real data indicate the usefulness of discrete stationary wavelet transform in order to optimal separation of anomalies with various wavelengths.

Paper Nr: 4
Title:

Artificial Neural Network based Methodologies for the Spatial and Temporal Estimation of Air Temperature - Application in the Greater Area of Chania, Greece

Authors:

Despina Deligiorgi, Kostas Philippopoulos and Georgios Kouroupetroglou

Abstract: Artificial Neural Networks (ANN) propose an alternative promising methodological approach to the problem of time series assessment as well as point spatial interpolation of irregularly and gridded data. ANNs can be used as function approximators to estimate both the time and spatial air temperature distributions based on observational data. After reviewing the theoretical background as well as the relative advantages and limitations of ANN methodologies applicable to the field of air temperature time series and spatial modelling, this work focuses on implementation issues and on evaluating the accuracy of the AAN methodologies using a set of metrics in the case of a specific region with complex terrain. A number of alternative feed forward ANN topologies have been applied in order to assess the spatial and time series air temperature prediction capabilities in different horizons. For the temporal forecasting of air temperature ANNs were trained using the Levenberg-Marquardt back propagation algorithm with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. For the spatial estimation of air temperature the Radial Basis Function and Multilayer Perceptrons non-linear Feed Forward AANs schemes are compared. The underlying air temperature temporal and spatial variability is found to be modeled efficiently by the ANNs.

Short Papers
Paper Nr: 5
Title:

A Multi-features Fusion of Multi-temporal Hyperspectral Images using a Cooperative GDD/SVM Method

Authors:

Selim Hemissi and Imed Riadh Farah

Abstract: Considering the emergence of hyperspectral sensors, feature fusion has been more and more important for images classification, indexing and retrieval. In this paper, a cooperative fusion method GDD/SVM (Generalized Dirichlet Distribution/Support Vector Machines), which involves heterogeneous features, is proposed for multi-temporal hyperspectral images classification. It differentiates, from most of the previous approaches, by incorporating the potentials of generative models into a discriminative classifier. Therefore, the multi-features, including the 3D spectral features and textural features, can be integrated with an efficient way into a unified robust framework. The experimental results on a series of Hyperion images confirm the improved performance and show that this cooperative fusion approach has consistence over different testing datasets.

Paper Nr: 6
Title:

Characterization of Lithostratigraphic Units using Neuro Fuzzy System Analyses Applied to Rock Magnetic Data

Authors:

Nuri Hurtado, Vincenzo Costanzo-Álvarez, Diego López-Rodríguez, Milagrosa Aldana and Germán Bayona

Abstract: In this work we employ the Neuro Fuzzy System hybrid algorithm to infer S-ratio through the experimental magnetic susceptibility data measured in 90 samples, from a 670 meters - thick sedimentary sequence, at the stratigraphic well Saltarin 1A (Colombia). The method is applied here as a means for pattern recognition of the major lithostratigraphic units encompassed by this well (i.e. Guayabo, León and Cabonera Miocene Formations). The sets of fuzzy rules obtained work well only when used to infer S-ratios within the same Formation from which they were derived. This is particularly noticeable in Guayabo, with lithological characteristics different to those of León and Carbonera. The contrasts between these three Formations seem to be responsible for the inability of finding a unique set of fuzzy rules that could properly infer S-ratio over the whole well using experimental magnetic susceptibility data only as the input variable.

Posters
Paper Nr: 2
Title:

Application of Edge and Line Detection to Detect the near Surface Anomalies in Potential Data

Authors:

Lenka Kosková Třísková and Josef Novák

Abstract: Presented paper is focused on fast near surface anomaly detection in potential data. Our aim is to find fast and semi –automated anomaly detection technique for the near surface anomalies with defined geometry. The proposed algorithm is based on the shape recognition. The edge and line detection is used on acquired data to detect the typical shape of the anomaly. Shape geometry parameters are converted into the anomaly parameters and location information. The technique was tested using a set of noise-free and noisy synthetic gravity data; satisfactory results were obtained.

Paper Nr: 7
Title:

Edges Detection from Aeromagnetic Data using the Wavelet Transform

Authors:

Sid-Ali Ouadfeul and Leila Aliouane

Abstract: The main goal of this paper is to use the 2D Directional Continuous Wavelet Transform (DCWT) for structural boundaries delimitation from geomagnetic data. The proposed idea is based on the mapping of maxima of the modulus of the 2D DCWT for each scale used in the DCWT calculation. Application to synthetic data shows robustness of the technique. Application to the real geomagnetic data of In Ouzzal area located in the West of Hoggar (Algeria) shows clearly the strength of this last. Comparison with the analytic signal solutions exhibits that the DCWT is able to predict a pattern of boundary that is hidden by the noise in the analytic signal and eliminated by a threshold. The proposed method proves to be more powerful easy to use and versatile where classical methods of potential field interpretation fail or are very constraining.

Paper Nr: 8
Title:

Lithofacies Prediction from Well Logs Data using Different Neural Network Models

Authors:

Leila Aliouane, Sid-Ali Ouadfeul, Noureddine Djarfour and Amar Boudella

Abstract: The main objective of this work is to predict lithofacies from well-logs data using different artificial neural network (ANN) models. The proposed technique is based on three classifiers types of ANN which are the self-organizing map (SOM), multilayer Perceptron (MLP) and radial basis function (RBF). The data set as an input of the neural network machines are the eight borehole measurements which are the total natural gamma ray; the three concentrations of the radioactive elements Thorium, Potassium and Uranium; the slowness of the P wave, the bulk density, the neutron porosity and the photoelectric absorption coefficient of two boreholes located in Algerian Sahara. Hence, the outputs of three neuronal kinds are the different lithological classes of clayey reservoir. These classes are obtained by supervised and unsupervised learning. The output results compared with basic stratigraphy show that the Kohonen map gives the best lithofacies classification where the thin beds intercalated in the reservoir, are identified. Consequently, the neural network technique is a powerful method which provides an automatic classification of the lithofacies reservoir.