In axial compressors with a relatively large blade tip clearance, an unsteady phenomenon denoted as rotating instability (RI) can be detected when the compressor is throttled to the operating points near the stability limit. In the frequency domain, RIs are shown as a hump lower than the blade passing frequency. This indicates an increase in noise level and might cause blade vibration and other undesirable structural issues. In this thesis, a comprehensive study on RIs is performed based on an axial compressor rotor row of the Low Speed Research Compressor at Technische Universität Dresden. Three blade tip clearances are investigated, and a groove casing treatment is mounted over the shroud for flow control. Methods of numerical modeling are evaluated, and zonal large eddy simulation is selected as the numerical model. By analyzing the flow properties and applying the dynamic mode decomposition, the coherent flow structure corresponding to the dominant frequency of RIs is extracted and visualized as the waves located in the blade tip region. The criteria for the appearance of RIs in the investigated research object are concluded.

付記する学位プログラム名: 京都大学卓越大学院プログラム「先端光・電子デバイス創成学」 / 京都大学 / 新制・課程博士 / 博士(工学) / 甲第25290号 / 工博第5249号 / 新制||工||1999(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 阪本 卓也, 教授 引原 隆士, 准教授 薄 良彦, 教授 土居 伸二 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The research presented in this thesis serves to provide a tool to autonomously screen for cardiovascular disease in the rural areas of Africa. Vital information thus obtained from patients can be communicated to advanced medical centres by Telemedicine. Cardiovascular disease is then detected in its initial stages, which is essential to its effective treatment. The system developed in this study uses recorded heart sounds and electrocardiogram signals to distinguish between normal and abnormal heart conditions. This system improves on standard diagnostic tools in that it does not require cumbersome and expensive imaging equipment or a highly trained operator. Heart sound- and electrocardiogram signals from 62 volunteers were recorded with the prototype Precordialcardiogram device as part of a clinical study to aid in the development of the autonomous auscultation software and to screen patients for cardiovascular disease. These volunteers consisted of 28 patients of Tygerberg Hospital with cardiovascular disease and, for control purposes, 34 persons with normal heart conditions. The autonomous auscultation system developed during this study, interprets data obtained with the Precordialcardiogram device to autonomously acquire a normal or abnormal diagnosis. The system employs wavelet soft thresholding to denoise the recorded signals, followed by the segmentation of heart sound by identifying peaks in the electrocardiogram. Novel frequency spectral information was extracted as features from the heart sounds, by means of ensemble empirical mode decomposition and auto regressive modelling. These features proved to be particularly significant and played a major role in the screening capability of the system. New time domain based features were identified, established on the specific characteristics of the various cardiovascular diseases encountered during the study. These features were extracted via the energy ratios between different parts of ventricular systole and diastole of each recorded cardiac cycle. The respective features were classified to characterise typical heart diseases as well as healthy hearts with an ensemble artificial neural network. Herein the decisions of all the members were combined to obtain a final diagnosis. The performance of the autonomous auscultation system used in concert with the Precordialcardiogram device prototype, as determined through the leave-one-out crossvalidation method, had a sensitivity rating of 82% and a specificity rating of 88%. These results demonstrate the potential benefit of the Precordialcardiogram device and the developed autonomous auscultation software in a Telemedicine environment. / AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die navorsing van 'n outonome toetsing en sifting stelsel vir kardiovaskulêre siektes in landelike dele van Afrika, vanwaar mediese inligting per telefoon versend kan word. Die apparaat maak vroeë opsporing van kardiovaskulêre siektes moontlik, wat essensieel is vir effektiewe behandeling daarvan en ook die koste-effek van hierdie siektes verminder. In die huidige ontwikkelde stelsel word normale sowel as abnormale hart-toestande getipeer met opnames van hartklanke sowel as elektrokardiogram-seine. Voordele wat hierdie stelsel bo standaard diagnostiese metodes het, sluit die hanteerbare formaat van die hele apparaat sowel as die nie-noodsaaklikheid van duur beeldskeppende apparaat, of hoogs opgeleide personeel. Hartklank- en elektrokardiogramseine van 62 vrywilligers is met die prototipe "Precordialcardiogram" apparaat opgeneem om by te dra tot die ontwikkeling van die rekenaar sagteware vir die outonome auscultatsie stelsel en om die pasiëntsiftingsvermoë daarvan te toets. Die vrywilligers het 28 pasiënte van Tygerberg hospitaal met abnormale harttoestande ingesluit, sowel as ‘n kontrolegroep van 34 persone met normale harttoestande. Die outonome auskultasie-stelsel wat tot stand gekom het deur hierdie ondersoek maak gebruik van “wavelet” sagte drempeling om geraas uit die opgeneemde seine te verwyder. Daarna word die hartklanke gesegmenteer deur die pieke van die elektrokardiogram te identifiseer. Deur middel van "ensemble empirical mode decomposition" en outoregressiewe modellering, is nuwe inligting aangaande die frekwensie spektra van hartklanke, aanwysend van spesifieke harttoestande, verkry. Die beduidendheid van hierdie eienskappe is bewys en het 'n belangrike rol in die siftingsvermoë van die stelsel gespeel. Hierbenewens is nuwe tyd-gebaseerde eienskappe van die onderskeie kardiovaskulêre siektes wat tydens die ondersoek bestudeer is, geïdentifiseer. Hierdie eienskappe is geëien deur die energie-verhoudings tussen verskillende dele van die ventrikulêre sistolie en diastolie van elke opgeneemde hartsiklus te ontleed. 'n "Ensemble artificial neural network" is gebruik om die geïdentifiseerde eienskappe van hartsiektes sowel as normale harttoestande, te klassifiseer. Hierin is besluite van al die lede van die netwerk gekombineer, ten einde ‘n finale diagnose te maak. Die klassifiseerder se geldigheid is kruis-bevestig deur middel van die laat-een-uit kruisbevestigings-metode. Deur middel van die kruis-bevestigingsmetode is die bedryfsvermoëns van die outonome auskultasie-stelsel, toegerus met die "Precordialcardiogram" apparaat, repektiewelik op 82% vir sensitiwiteit en 88% vir spesifisiteit vasgestel. Hierdie resultate demonstreer die benuttingspotensiaal van die apparaat in 'n Telemedisyne omgewing.

This thesis presents a study on time series analysis, which was conducted based on the following hypothesis: time series influenced by additive noise can be decomposed into stochastic and deterministic components in which individual models permit obtaining a hybrid one that improves accuracy. This hypothesis was confirmed in two steps. In the first one, we developed a formal analysis using the Nyquist-Shannon sampling theorem, proving Intrinsic Mode Functions (IMFs) extracted from the Empirical Mode Decomposition (EMD) method can be combined, according to their frequency intensities, to form stochastic and deterministic components. Considering this proof, we designed two approaches to decompose time series, which were evaluated in synthetic and real-world scenarios. Experimental results confirmed the importance of decomposing time series and individually modeling the deterministic and stochastic components, proving the second part of our hypothesis. Furthermore, we noticed the individual analysis of both components plays an important role in detecting patterns and extracting implicit information from time series. In addition to these approaches, this thesis also presents two new measurements. The first one is used to evaluate the accuracy of time series modeling in forecasting observations. This measurement was motivated by the fact that existing measurements only consider the perfect match between expected and predicted values. This new measurement overcomes this issue by also analyzing the global time series behavior. The second measurement presented important results to assess the influence of the deterministic and stochastic components on time series observations, supporting the decomposition process. Finally, this thesis also presents a Systematic Literature Review, which collected important information on related work, and two new methods to produce surrogate data, which permit investigating the presence of linear and nonlinear Gaussian processes in time series, irrespective of the influence of nonstationary behavior / Esta tese apresenta um estudo sobre análise de séries temporais, a qual foi conduzida baseada na seguinte hipótese: séries temporais influenciadas por ruído aditivo podem ser decompostas em componentes estocásticos e determinísticos que ao serem modelados individualmente permitem obter um modelo híbrido de maior acurácia. Essa hipótese foi confirmada em duas etapas. Na primeira, desenvolveu-se uma análise formal usando o teorema de amostragem proposto por Nyquist-Shannon, provando que IMFs (Intrinsic Mode Functions) extraídas pelo método EMD (Empirical Mode Decomposition) podem ser combinadas de acordo com suas intensidades de frequência para formar os componentes estocásticos e determinísticos. Considerando essa prova, duas abordagens de decomposição de séries foram desenvolvidas e avaliadas em aplicações sintéticas e reais. Resultados experimentais confirmaram a importância de decompor séries temporais e modelar seus componentes estocásticos e determinísticos, provando a segunda parte da hipótese. Além disso, notou-se que a análise individual desses componentes possibilita detectar padrões e extrair importantes informações implícitas em séries temporais. Essa tese apresenta ainda duas novas medidas. A primeira é usada para avaliar a acurácia de modelos utilizados para predizer observações. A principal vantagem dessa medida em relação às existentes é a possibilidade de avaliar os valores individuais de predição e o comportamento global entre as observações preditas e experadas. A segunda medida permite avaliar a influência dos componentes estocásticos e determinísticos sobre as séries temporais. Finalmente, essa tese apresenta ainda resultados obtidos por meio de uma revisão sistemática da literatura, a qual coletou importantes trabalhos relacionados, e dois novos métodos para geração de dados substitutos, permitindo investigar a presença de processos Gaussianos lineares e não-lineares, independente da influência de comportamento não-estacionário

La combustion prévaporisée prémélangée pauvre est une piste de choix pour réduire les émissions polluantes des moteurs d'avions mais peut conduire à l'apparition d'instabilités thermo-acoustiques. Afin d'améliorer la stabilité de telles flammes, l'étagement du combustible consiste à contrôler la distribution spatiale du carburant. Une telle procédure s'accompagne cependant d'une complexité accrue du système pouvant déboucher sur des phénomènes inattendus.Un brûleur à l'échelle de laboratoire alimenté par du dodécane liquide est utilisé dans cette thèse. Le combustible est injecté dans deux étages séparés, permettant ainsi de contrôler sa distribution. Cette particularité permet l'observation de différentes formes de flammes et notamment de points bistables pour lesquels deux flammes différentes peuvent exister malgré des conditions opératoires identiques.L'utilisation de diagnostics optiques à haute cadence (diffusion de Mie des gouttes de combustible et émission spontanée de la flamme) est couplée à des méthodes de post-traitement avancées comme la Décomposition en Modes Dynamiques. Ainsi, des mécanismes pilotant la stabilisation des flammes ainsi que leurs changements de forme sont proposés. Ils mettent notamment en lumière les interactions entre l'écoulement gazeux, les gouttes de combustible et la flamme. / A promising way to reduce jet engines pollutant emissions is the use of lean premixed prevaporized combustion but it tends to trigger thermo-acoustic instabilities. To improve the stability of these flames, a procedure called staging consists in splitting the fuel injection to control its spatial distribution. This however leads to an increased complexity and unexpected phenomena can occur.In the present work, a model gas turbine combustor fed with liquid dodecane is used. It is equipped with two fuel injection stages to control the fuel distribution in the burner. Different flame stabilizations can be observed and a bistable case where two flame shapes can exist for the same operating conditions is highlighted.High-speed optical diagnostics (fuel droplets Mie scatering and chemiluminescence measurements) are coupled with advanced post-processing methods like Dynamic Mode Decomposition. The results enable to propose mechanisms leading to flame stabilization and flame shape transitions. They show a strong interplay between the gaseous flow, the fuel droplets and the flame itself.

Cette thèse dans le domaine du traitement des signaux non-stationnaires, appliqué aux bruits du cœur mesurés avec un stéthoscope numérique, vise à concevoir un outil automatisé et « intelligent », permettant aux médecins de disposer d’une source d’information supplémentaire à celle du stéthoscope traditionnel. Une première étape dans l’analyse des signaux du cœur, consiste à localiser le premier et le deuxième son cardiaque (S1 et S2) afin de le segmenter en quatre parties : S1, systole, S2 et diastole. Plusieurs méthodes de localisation des sons cardiaques existent déjà dans la littérature. Une étude comparative entre les méthodes les plus pertinentes est réalisée et deux nouvelles méthodes basées sur la transformation temps-fréquence de Stockwell sont proposées. La première méthode, nommée SRBF, utilise des descripteurs issus du domaine temps-fréquence comme vecteur d’entré au réseau de neurones RBF qui génère l’enveloppe d’amplitude du signal cardiaque, la deuxième méthode, nommée SSE, calcule l’énergie de Shannon du spectre local obtenu par la transformée en S. Ensuite, une phase de détection des extrémités (onset, ending) est nécessaire. Une méthode d’extraction des signaux S1 et S2, basée sur la transformée en S optimisée, est discutée et comparée avec les différentes approches qui existent dans la littérature. Concernant la classification des signaux cardiaques, les méthodes décrites dans la littérature pour classifier S1 et S2, se basent sur des critères temporels (durée de systole et diastole) qui ne seront plus valables dans plusieurs cas pathologiques comme par exemple la tachycardie sévère. Un nouveau descripteur issu du domaine temps-fréquence est évalué et validé pour discriminer S1 de S2. Ensuite, une nouvelle méthode de génération des attributs, basée sur la décomposition modale empirique (EMD) est proposée.Des descripteurs non-linéaires sont également testés, dans le but de classifier des sons cardiaques normaux et sons pathologiques en présence des souffles systoliques. Des outils de traitement et de reconnaissance des signaux non-stationnaires basés sur des caractéristiques morphologique, temps-fréquences et non linéaire du signal, ont été explorés au cours de ce projet de thèse afin de proposer un module d’aide au diagnostic, qui ne nécessite pas d’information à priori sur le sujet traité, robuste vis à vis du bruit et applicable dans des conditions cliniques. / This thesis in the field of biomedical signal processing, applied to the heart sounds, aims to develop an automated and intelligent module, allowing medical doctors to have an additional source of information than the traditional stethoscope. A first step in the analysis of heart sounds is the segmentation process. The heart sounds segmentation process segments the PCG (PhonoCardioGram) signal into four parts: S1 (first heart sound), systole, S2 (second heart sound) and diastole. It can be considered one of the most important phases in the auto-analysis of PCG signals. The proposed segmentation module in this thesis can be divided into three main blocks: localization of heart sounds, boundaries detection of the localized heart sounds and classification block to distinguish between S1and S2. Several methods of heart sound localization exist in the literature. A comparative study between the most relevant methods is performed and two new localization methods of heart sounds are proposed in this study. Both of them are based on the S-transform, the first method uses Radial Basis Functions (RBF) neural network to extract the envelope of the heart sound signal after a feature extraction process that operates on the S-matrix. The second method named SSE calculates the Shannon Energy of the local spectrum calculated by the S-transform for each sample of the heart sound signal. The second block contains a novel approach for the boundaries detection of S1 and S2 (onset & ending). The energy concentrations of the S-transform of localized sounds are optimized by using a window width optimization algorithm. Then the SSE envelope is recalculated and a local adaptive threshold is applied to refine the estimated boundaries. For the classification block, most of the existing methods in the literature use the systole and diastole duration (systole regularity) as a criterion to discriminate between S1 and S2. These methods do not perform well for all types of heart sounds, especially in the presence of high heart rate or in the presence of arrhythmic pathologies. To deal with this problem, two feature extraction methods based on Singular Value Decomposition (SVD) technique are examined. The first method uses the S-Transform and the second method uses the Intrinsic Mode Functions (IMF) calculated by the Empirical Mode Decomposition (EMD) technique. The features are applied to a KNN classifier to estimate the performance of each feature extraction method. Nonlinear features are also tested in order to classify the normal and pathological heart sounds in the presence of systolic murmurs. Processing and recognition signal processing tools based on morphological, time-frequency and nonlinear signal features, were explored in this thesis in order to propose an auto-diagnosis module, robust against noise and applicable in clinical conditions.

La Décomposition Modale Empirique (EMD pour « Empirical Mode Decomposition ») est un outil récent de traitement du signal dévolu à l'analyse de signaux non stationnaires et/ou non linéaires. L'EMD produit pour tout signal une décomposition multi-échelles pilotée par les données. Les composantes obtenues sont des formes d'onde oscillantes potentiellement non harmoniques dont les caractéristiques, forme, amplitude et fréquence peuvent varier au cours du temps. L'EMD étant une méthode encore jeune, elle n'est définie que par la sortie d'un algorithme inhabituel, comportant de multiples degrés de liberté et sans fondement théorique solide. Nous nous intéressons dans un premier temps à l'algorithme de l'EMD. Nous étudions d'une part les questions soulevées par les choix de ses degrés de liberté afin d'en établir une implantation. Nous proposons d'autre part des variantes modifiant légèrement ses propriétés et une extension permettant de traiter des signaux à deux composantes. Dans un deuxième temps, nous nous penchons sur les performances de l'EMD. L'algorithme étant initialement décrit dans un contexte de temps continu, mais systématiquement appliqué à des signaux échantillonnés, nous étudions la problématique des effets d'échantillonnage sur la décomposition. Ces effets sont modélisés dans le cas simple d'un signal sinusoïdal et une borne de leur influence est obtenue pour des signaux quelconques. Enfin nous étudions le mécanisme de la décomposition à travers deux situations complémentaires, la décomposition d'une somme de sinusoïdes et celle d'un bruit large bande. Le premier cas permet de mettre en évidence un modèle simple expliquant le comportement de l'EMD dans une très grande majorité des cas de sommes de sinusoïdes. Ce modèle reste valide pour des sinusoïdes faiblement modulées en amplitude et en fréquence ainsi que dans certains cas de sommes d'ondes non harmoniques périodiques. La décomposition de bruits large bande met quant à elle en évidence un comportement moyen de l'EMD proche de celui d'un banc de filtres auto-similaire, analogue à ceux correspondant aux transformées en ondelettes discrètes. Les propriétés du banc de filtres équivalent sont étudiées en détail en fonction des paramètres clés de l'algorithme de l'EMD. Le lien est également établi entre ce comportement en banc de filtres et le modèle développé dans le cas des sommes de sinusoïdes.

Η διπλωματική εργασία με τίτλο «Μέθοδοι για Ανίχνευση και Χαρακτηρισμό Βιοσημάτων σε Θορυβώδεις Χρονοσειρές βασισμένοι στο Μετασχηματισμό Hilbert-Huang» μελετάει ζητήματα που σχετίζονται με βιοϊατρικά σήματα και την ανάλυση τους. Γίνεται διερεύνηση των διαθέσιμων τεχνικών και μεθόδων ανάλυσης βιοϊατρικών σημάτων, επισημαίνονται τα ιδιαίτερα χαρακτηριστικά των χρονοσειρών που προκύπτουν από την παρατήρηση και καταγραφή των σημάτων και έμφαση δίνεται στη μη στασιμότητα, την μη γραμμικότητα των υποκείμενων φυσικών διεργασιών και την ανάγκη προσαρμοστικότητας της μεθόδου. Μια μέθοδος που ικανοποιεί αυτές τις απαιτήσεις είναι η εμπειρική μέθοδος αποσύνθεσης η οποία αναλύει ένα σήμα σε ένα σύνολο συνιστωσών (IMFs) από τις οποίες ένα υποσύνολο θεωρείται ότι έχει φυσική σημασία. Επιπλέον, με το μετασχηματισμό Hilbert ανιχνεύονται οι στιγμιαίες συχνότητες και διαμορφώνεται η χρονοσυχνοτική κατανομή του σήματος. Τα θέματα που διερευνώνται αναφορικά με την εμπειρική μέθοδο αποσύνθεσης αφορούν τη στατιστική σημαντικότητα των IMFs, την αποθορυβοποίηση βιοϊατρικών σημάτων, την εξαγωγή χαρακτηριστικών από ηλεκτροκαρδιογράφημα και την απόδοση της μεθόδου. Ειδικά η απόδοση της εμπειρικής μεθόδου αποσύνθεσης είναι κρίσιμη παράμετρος για συστήματα με περιορισμένους πόρους όπως είναι οι κόμβοι ασύρματων δικτύων αισθητήρων ή τα ενσωματωμένα συστήματα. Η μοντελοποίηση μεθόδων που υλοποιούνται στο επίπεδο κόμβων ασύρματου δικτύου αισθητήρων είναι απαραίτητη για τη βέλτιστη διαχείριση πόρων και τον προγραμματισμό διεργασιών ώστε να μην διαταραχθεί η λειτουργία και λειτουργικότητα του συστήματος / This diploma thesis entitled "Methods for Identification and Characterization of Biosignals in Noise corrupted Time Series based on Hilbert-Huang Transform " studies issues concerning biomedical signal analysis. There is a review of the available techniques and methods for biomedical signal analysis pointing at certain characteristics of biomedical time series such as non stationarity, the non linearity of the underlying physical process and the need for the adaptive nature of the analysis method. One method that meets these requirements is considered to be the Empirical Mode Decomposition (EMD) which decomposes a signal into a set of components (IMFs) that a subset of them is believed to have a physical meaning. Application of Hilbert Transform on these IMFs provides the instantaneous frequencies and forms the time-frequency distribution of the signal. Issues studied are related to the statistical significance of the IMFs, denoising of biomedical signals, characteristics extraction and feature selection out of the electrocardiogram as well as the performance of the method. Particularly, the performance of empirical mode decomposition is considered to be a critical parameter especially in the case of implementation on nodes of wireless sensor networks or generally embedded systems due to the limited amount of resources available onboard. Modeling method's performance and demand for resources is a significant task facilitating the optimum resource management and task execution schedule of these systems.

Combustion instability due to feedback coupling between unsteady heat release and natural acoustic modes can cause catastrophic failure in liquid rocket engines and to predict and prevent these instabilities the mechanisms that drive them must be further elucidated. With this goal in mind, the objective of this thesis was to develop techniques that improve the understanding of the specific underlying physical processes involved in these driving mechanisms. In particular, this work sought to develop a small-scale, optically accessible liquid rocket engine simulator and to apply modern, high-speed diagnostic techniques to characterize the reacting flow and acoustic field within the simulator. Specifically, high-speed (10 kHz), simultaneous data were acquired while the simulator was experiencing a 170 Hz combustion instability using particle image velocimetry, OH planar laser induced fluorescence, CH* chemiluminescence, and dynamic pressure measurements. In addition, this work sought to develop approaches to reduce the large quantities of data acquired, extracting key physical phenomena involved in the driving mechanisms. The initial data reduction approach was chosen based on the fact that the combustion instability problem is often simplified to the point that it can be characterized by an approximately linear constant coefficient system of equations. Consistent with this simplification, the experimental data were analyzed by the dynamic mode decomposition method. The developed approach to apply the dynamic mode decomposition to simultaneously acquired data located a coupled hydrodynamic/combustion/acoustic mode at 1017 Hz. On the other hand, the dynamic mode decomposition's assumed constant operator approach failed to locate any modes of interest near 170 Hz. This led to the development of two new data analysis techniques based on the dynamic mode decomposition and Floquet theory that assume that the experiment is governed by a linear, periodic system of equations. The new periodic-operator data analysis techniques, the Floquet decomposition and the ensemble Floquet decomposition, approximate, from experimental data, the largest moduli Floquet multipliers, which determine the stability of the periodic solution trajectory of the system. The unstable experiment dataset was analyzed with these techniques and the ensemble Floquet decomposition analysis found a large modulus Floquet multiplier and associated mode with a frequency of 169.6 Hz. Furthermore, the approximate Rayleigh criterion indicated that this mode was unstable with respect to combustion instability. Overall, based on the positive finding that the ensemble Floquet decomposition was able to locate an unstable combustion mode at 170 Hz when the operator's time period was set to 1 ms, suggests that the dynamic mode decomposition based 1017 Hz mode parametrically forces the 170 Hz mode, resulting in what could be characterized as a parametric combustion instability.

Made available in DSpace on 2014-12-17T14:08:40Z (GMT). No. of bitstreams: 1 FelipeZA_DISSERT.PDF: 5156580 bytes, checksum: cf88acbbf99c9d93a555a758d3e21bf5 (MD5) Previous issue date: 2010-10-23 / The seismic processing technique has the main objective to provide adequate picture of geological structures from subsurface of sedimentary basins. Among the key steps of this process is the enhancement of seismic reflections by filtering unwanted signals, called seismic noise, the improvement of signals of interest and the application of imaging procedures. The seismic noise may appear random or coherent. This dissertation will present a technique to attenuate coherent noise, such as ground roll and multiple reflections, based on Empirical Mode Decomposition method. This method will be applied to decompose the seismic trace into Intrinsic Mode Functions. These functions have the properties of being symmetric, with local mean equals zero and the same number of zero-crossing and extremes. The developed technique was tested on synthetic and real data, and the results were considered encouraging / O processamento s?smico tem como principal objetivo fornecer imagem adequada das estruturas geol?gicas da sub-superf?cie de bacias sedimentares. Dentre as etapas fundamentais deste processamento est? o enriquecimento das reflex?es s?smicas atrav?s de filtragem de sinais indesej?veis, chamados de ru?dos, a amplifica??o de sinais de interesse e a aplica??o de processos de imageamento. Os ru?dos s?smicos podem aparecer de forma aleat?ria ou coerente. Nesta disserta??o ser? apresentado uma t?cnica para atenuar ru?dos coerentes, como o ground roll e as reflex?es m?ltiplas, baseado na Decomposi??o em Modos Emp?ricos. Este m?todo consiste em decompor o tra?o s?smico em Fun??es de Modo Intr?nseco, que s?o fun??es sim?tricas com m?dia local igual a zero e mesmo n?mero de zeros e extremos. A t?cnica desenvolvida foi testado em dados sint?ticos e reais, e os resultados obtidos foram considerados encorajadores