Abstract:
This article is a practical review of the different wave separation methods used in seismic. A wave is described by its propagation vector and its wavelet. The first part of the article shows how the propagation vector can be used to define both the type of propagation (plane or nonplane wave) and the characteristics of the medium (dispersive or faulted medium). Wave separation and wave-type identification can then be dealt with by studying the scalar product between two waves or between a wave and a reference model. The main filtering methods are described, in particular : f-k filtering, tau-p filtering, Karhunen-Loeve filtering and spectral matrix filtering. The efficiency of the different methods is assessed with synthetic data. The problem of extracting a wave from noise is also discussed and illustrated with a field example. The second part will be devoted to wave separation per se. The different methods described in the first part are applied to real data, in particular borehole survey seismic data. Special attention is given to the use of specific, less well-known methods such as spectral matrix filtering with adapted or constrained models. Cet article est une revue pratique des différentes méthodes de séparation d'ondes utilisées en sismique. Une onde est décrite par son vecteur de propagation et par son ondelette. La première partie montre comment le vecteur de propagation peut être utilisé à la fois pour définir le type de propagation (onde plane ou non plane) et les caractéristiques du milieu (milieu dispersif ou faillé). La séparation d'ondes et l'identification d'un type d'onde peuvent alors être abordées par l'étude du produit scalaire entre deux ondes ou entre une onde et un modèle de référence. Les principales méthodes de filtrage sont décrites : notamment le filtrage (f, k), le filtrage (tau, p), le filtrage de Karhunen-Loève et le filtrage par la matrice interspectrale. L'efficacité des différentes méthodes est évaluée sur données synthétiques. Le problème de l'extraction d'une onde du bruit est également discuté et illustré sur un exemple réel. La deuxième partie sera consacrée à la séparation des ondes proprement dites. Les différentes méthodes décrites dans la première partie sont appliquées à des données réelles, notamment des données de sismique de puits. Une attention particulière est portée sur l'utilisation de méthodes spécifiques et moins connues telles que le filtrage par la matrice interspectrale avec modèles adaptés ou contraints.

Abstract:
Identifying waves in seismic sections sometimes requires the waves to be separated. The geophysicist has a variety of complementary filters at his disposal that can be used to perform optimum separations if they are carefully chosen and combined. The first part of this article was devoted to the principle and methods of wave separation. Wave separation methods can be divided up into three categories : acceptance region methods, inversion methods and matrix methods. The tau-p method and f-k filtering belong to the first category while the parametric method belongs to the second one. Matrix filtering by means of the cross-spectral matrix (SMF : Spectral Matrix Filtering), the singular value decomposition (SVD) and the Karhunen-Loeve method (KLT-Karhunen-Loeve Transform) belong to the third group. Matrix methods are used both to separate waves and to break data down into a signal space and a noise space. Here in the second part, we use synthetic data to compare how well the SVD and SMF methods perform in separating waves with only one eigenvector. We show that SMF filtering can be made much more effective by introducing models and present the SMF method with adapted or constrained models. We also introduce a field example of wave separation by conventional SMF filtering, then a synthetic example and two field examples of wave separation by SMF filtering with models. We demonstrate the advantages of using different wave separation methods together (f-k, KLT and SMF) to achieve optimum separation. The data that serve to illustrate this are full waveform acoustic data acquired in a horizontal drain hole. A VSP-type well survey is used to compare the different methods : f-k, SVD, SMF and the parametric method. The last example shows how SMF processing can be used for anisotropy measurement. The f-k filter requires a large number of traces that have been distance sampled at short intervals. The more stable the wave that is being extracted and the more clearly located it is in the f-k domain, the more efficient the filter is. The method is very cost-effective in CPU time. The KLT or SVD filter requires flattening the wave that is to be extracted, which must additionally be of greater amplitude. Filtering is carried out without any edge effect and the wave amplitude variations are preserved. It serves to separate the normal incidence wave from the other waves and the noise. The SMF filter (spectral matrix) is expensive in CPU time It makes the hypothesis that the wave is locally stable and does not require the data to be flattened. It can be used to separate very

Abstract:
The spectral matrix computed from VSP-traces transfer functions contains information about each wave making up the VSP data set. Using a filter based on the eigenvectors of the spectral matrix leads to a decomposition of input traces in eigensections. The eigensections associated with the largest eigenvalues contain the contribution of the correlated seismic events. Signal space is denoted as the sum of these eigensections. Other eigensections represent noise. When the different waves making up the VSP have very different amplitudes, decomposition of input traces into eigensections leads to wave separation without any required knowledge about the apparent velocities of the waves. Limitations of wave separation by the multichannel filtering are a function of the scalar product values of the waves (in frequency domain) and of the relative wave amplitudes. The spectral matrix filtering can always be used to enhance signal-to-noise ratio on VSP data. The eigenvalues of the spectral matrix can be used to estimate the signal-to-noise ratio as a function of frequency. It is possible to qualify the behavior of a VSP tool in a well and to detect some resonant frequencies probably generated by poor coupling. Field data examples are shown. The first example shows data recorded in a vertical well whose converted shear waves are separated from upgoing and downgoing compressional waves using a spectral matrix filter. This field case shows the efficiency of the spectral matrix filter in extracting weak events. The second example shows data recorded in a highly deviated well, where very close apparent velocity events are successfully separated by use of spectral matrix filtering. La technique de filtrage matriciel, quel que soit le type de données auxquelles elle est appliquée, permet d'améliorer le rapport signal sur bruit, de quantifier l'évolution du rapport signal sur bruit en fonction de la fréquence, d'identifier les différents signaux composant les données et de séparer ces signaux. Nous montrons que les signaux peuvent être automatiquement séparés sans connaissance a priori sur leurs vitesses apparentes, en fonction du produit scalaire (calculé dans le domaine fréquentiel) et de l'amplitude relative des signaux. Nous montrons des exemples d'application sur des données de sismique de puits. Le filtrage matriciel est effectué dans le domaine fréquentiel en utilisant la matrice spectrale construite à l'aide des intercorrélations des différents enregistrements constituant les données à traiter. Les méthodes d'estimation de la matrice spectrale sont des méthodes de m

Abstract:
Most multichannel algorithms used for separation of upgoing and downgoing waves in VSPs assume depth-stationarity of the signal on all the traces used in the separation filter. However, if the depth-window of the filters becomes too large (100-200 m) signal stationarity cannot be assumed. On the other hand, stationarity is a physically reasonable assumption for two neighbouring probe locations (5-20 m). A comparison is made of three algorithms used for the separation of upgoing and downgoing waves, that require only two adjacent traces and their first arrival times. They are independent of in-depth trace-spacing, provided there are no geological discontinuities between adjacent traces. The first approach (near-theoretical solution) operates in the frequency domain. A system of two equations and two unknowns is solved for every frequency within the best coherencebandwidth. The second approach (simple solution) is a delay-and-sum, and subtraction filter based on the semblance of the signals. The third approach (Wiener solution) uses a Wiener filter to predict the strongest wave, which is generally the downgoing wave. The upgoing wave is then obtained by subtracting the estimated downgoing wave from the full wave record. A second application of the Wiener filter on the upgoing waves can enhance them. The near-theoretical solution and the simple solution are narrow-pass velocity filters and are applicable to zero-offset VSPs. The simple solution is suitable for noisy data. When the signal-to-noise ratio is high, the best results are obtained using the near-theoretical solution. The velocity-filter bandwidth can be increased in the Wiener solution, so that it can also be used in case of dipping reflectors or offset VSPs. The Wiener solution is suitable for noisy data, and its effectiveness can be increased by using a reference trace. The performance of these algorithms on synthetic and field data is evaluated in terms of signal-to-noise ratio, detection of upgoing waves and sensitivity to first arrival time estimates. La plupart des algorithmes utilisés pour la séparation des ondes montantes et descendantes en PSV font l'hypothèse que le signal est stationnaire en fonction de la profondeur sur toutes les traces utilisées dans le filtre de séparation. Cependant, le signal ne peut plus être supposé stationnaire lorsque les filtres sont appliqués sur une fenêtre en profondeur trop grande (100-200 m). Par contre, la stationnarité du signal peut être considérée comme une hypothèse physiquement raisonnable pour deux positions voisines de la sonde (5-20 m). Cet arti

Abstract:
In the repercussions of the latest financial crisis that have occurred on the years 2008-2009, to fortify the stability of the banking systems, policy makers, and the Basel Committee on Banking Supervision—BCBS, together with national regulators have built up a few safety measures, and structures to guarantee that banks establishments keep up adequate capital levels through using risk management tools, in specific the Internal Capital Adequacy Assessment Processes (ICAAP). They all have called for thorough evaluations and assessments for the structure and components of risk management frameworks, tools, and practices whether by banks, regulators, analysts and risk management experts consistently, to ascertain the adequacy of the banking systems, policies, arrangements and techniques for overseeing risks, and guaranteeing the sufficiency of holding appropriate capital levels for confronting normal, as well as adverse and unexpected situations or emergencies. The main objectives of this research study are to shed the light on the ICAAP as one of the main keys of risk management programs, a process by which banks can use to ensure that they operate with an appropriate level of capital, forward looking processes for capital planning covering a broad range of risks across banks, activities beyond simple capital management, and bring together risk and capital management activities in a form that can be used to support business decisions. The research study shall evaluate the significant relationship between the Banking System Stability (dependent variable) and the Internal Capital Adequacy Assessment Process (ICAAP—independent variable) with evidence from the Egyptian Banking Sector.

Abstract:
The Earth’s surface roughness constitutes an important parameter in terrain analysis for studying different environmental and engineering problems. Authors gave different definitions and measures for the earth’s surface roughness that usually depend on exploitation of digital elevation data for its reliable determination. This research aimed at exploring the different approaches for defining and extraction of the Earth’s surface roughness from Airborne LiDAR Measurements. It also aimed at evaluating the effects of the window size of the standard deviation filter on the created roughness maps in downtown landscapes using three known approaches namely; standard deviation filtering of the Digital Elevation Model (DEM), standard deviation filtering of the slope gradient model and standard deviation filtering of the profile curvature model. In this context, different roughness maps have been created from Airborne LiDAR measurements of the City of Toronto, Canada using the three filtering approaches with varying window sizes. Visual analysis has shown color tones of small roughness values with smooth textures dominate the roughness maps from small window sizes of the standard deviation filter, however, increasing the window sizes has produced wider variations of the color tones and rougher texture roughness maps. The standard deviations and ranges of the roughness maps from LiDAR DEM have increased due to increasing the filter window size while the skewness and kurtosis have decreased due to increasing the window size, indicating that the roughness maps from larger window sizes are statistically more symmetrical and more consistent. Thus, kurtosis has decreased by 53% and 82% due to increasing the window size to 7 × 7 and 15 × 15 respectively. The standard deviations of the roughness maps from the slope gradient model have increased due to increasing the window size till 15 × 15 while they have decreased with more increases. However, skewness has decreased due to increasing the window size till 15 × 15 and the kurtosis has decreased with higher rate till window size of 11 × 11. In the roughness maps from the profile curvature model, the ranges and skewness have decreased by 93.6% and 82.6% respectively due to increasing the window size to 15 × 15 while, kurtosis has decreased by 58.6%, 76.3% and 93.76% due to increases in the filter window size to 5 × 5, 7 × 7 and 15 × 15 respectively.