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This book examines the effects of incoherent noise and how it leads to the misinterpretation of seismic data. It also reviews common noise reduction approaches and their drawbacks, focusing on developments that have occurred in the past decade. The main features of this book include: • Hands-on implementation in MATLAB and/or C • In-depth discussions of both theoretical and practical aspects of the subject • Supplementary, real-world seismic data • Detailed descriptions of structure-enhancing filters. Connecting the theory and practical implementation of noise reduction, the book helps readers fill the gap from equations to code, and from classical filters to the preservation and enhancement of a robust structure. Lastly, it highlights cutting-edge research in the area. As such, it is of interest to researchers in the fields of petroleum engineering, exploration seismology, and geophysics, as well as to practitioners working in the petroleum industry.
This book examines the effects of incoherent noise and how it leads to the misinterpretation of seismic data. It also reviews common noise reduction approaches and their drawbacks, focusing on developments that have occurred in the past decade. The main features of this book include: Hands-on implementation in MATLAB and/or C In-depth discussions of both theoretical and practical aspects of the subject Supplementary, real-world seismic data Detailed descriptions of structure-enhancing filters. Connecting the theory and practical implementation of noise reduction, the book helps readers fill the gap from equations to code, and from classical filters to the preservation and enhancement of a robust structure. Lastly, it highlights cutting-edge research in the area. As such, it is of interest to researchers in the fields of petroleum engineering, exploration seismology, and geophysics, as well as to practitioners working in the petroleum industry.
This volume attempts to examine the sequence of operations required for the extraction of the flow of messages from a background of random noise and unwanted signals. The analysis will involve the use of frontier integrals, autocorrelation, cross correlation, power spectral studies and convolution or filtering. Attention will be made to the definition of signal and noise and how these may change under different processing methods.
This book presents the essential principles and applications of seismic oil-exploration techniques. It concisely covers all stages in exploration activities (data field acquisition, data processing and interpretation), supplementing the main text with a wealth of (>350) illustrations and figures. The book concentrates on the physics of the applied principles, avoiding intricate mathematical treatment and lengthy theoretical reasoning. A further prominent feature is the inclusion of a separate chapter on 3D surveying techniques and another, equally important chapter on seismic digital signals and the aliasing problem, which is presented in an accessible form. The book is designed to meet the needs of both the academic and industrial worlds. University students and employees of oil-exploration companies alike will find the book to be a valuable resource.
Introducing the physical basis, mathematical implementation, and geologic expression of modern volumetric attributes including coherence, dip/azimuth, curvature, amplitude gradients, seismic textures, and spectral decomposition, the authors demonstrate the importance of effective colour display and sensitivity to seismic acquisition and processing.
This book reports on recent applications in biology and geoscience. Among them we mention the application of wavelet transforms in the treatment of EEG signals, the dimensionality reduction of the gait recognition framework, the biometric identification and verification. The book also contains applications of the wavelet transforms in the analysis of data collected from sport and breast cancer. The denoting procedure is analyzed within wavelet transform and applied on data coming from real world applications. The book ends with two important applications of the wavelet transforms in geoscience.
"In this thesis I have carried out a comprehensive study of the attenuation properties of the Earth́s crust of the Iberia-Morocco region (IMR). I have investigated the crustal attenuation by means of the quality factor Q, which is inversely proportional to attenuation, using both earthquakes and noise-derived measurements. In order to fulfill the thesis objectives a large dataset including earthquake waveforms and seismic noise records has been used. Three different traditional earthquake methods have been implemented to estimate Q in the IMR: the two-station (TS) method, the coda normalization (CN) method and the spectral amplitude decay (SAD) method. For the estimation of Q, these approaches measure the spectral amplitude of the Lg wave (direct and coda) of regional events. Among all the methods evaluated, the TS method allows imaging the spatial variation of the Lg wave attenuation in the Iberian Peninsula whereas the CN and the SAD methods only estimate average attenuation values as well as its frequency dependence. For the Iberian Peninsula, high Lg Q values are observed in the stable Iberian Massif in western Iberia, while lower values are mainly found in the Pyrenean Range and in eastern and southern Iberia. For Morocco, the CN and the SAD methods produce similar results, indicating that the Lg Q models are robust to differences in the methodologies. The frequency-dependent Q estimates represent an average attenuation across a broad region of different structural domains and correlate well with areas of moderate seismicity. Additionally, I have studied the Lg propagation efficiency across the IMR. Results reflect an inefficient or even blocked propagation across the Gulf of Cádiz and for most paths crossing the western Alboran basin. The continental crust of the Iberian Peninsula and Morocco shows efficient Lg propagation. I have also investigated the potential of using ambient noise measurements to retrieve information about the anelastic structure of the Earth́s crust. Since noise preprocessing techniques modify the amplitude of the recovered empirical Green function of the medium, additional tests have to be done in order to verify the reliability of the attenuation results obtained. In this regard, I have carefully examined the influence of the distribution of noise sources and receivers on Q estimates. Azimuthally and spatially averaged Q values derived from noise recordings were further compared with earthquake attenuation measurements. Results reveal that the average Q estimates are in concordance with previous long-period surface-wave measurements from earthquakes in the central part of the Iberian Peninsula. Accurate Q estimates are also found in Morocco. I would like to emphasize that this thesis presents new contributions and improvements to the knowledge of the attenuation structure of the IMR. The first regional map that images the lateral variation of the Lg Q has been estimated for the Iberian Peninsula improving the spatial resolution of earlier studies. The frequency dependence of Lg Q has been also calculated for the first time in Morocco. Furthermore, this work is the first attempt to recover attenuation information from ambient seismic noise measurements in the study area. This novel technique allows us to investigate the attenuation structure of the Earth without the occurrence of earthquakes. Exploiting ambient seismic wavefields for attenuation studies will be a powerful tool to extract information about the anelastic structure and the geodynamics in areas of very low seismicity in the near future. It should be also noticed that recovering crustal attenuation values is important for many reasons. Attenuation estimates can be used to better quantify the hazard associated with earthquake ground shaking. Attenuation is also a valuable property in exploration seismology. For example, the presence of fluids can significantly attenuate the amplitude of the seismic waves."--TDX.
Simultaneous shooting achieves a much faster seismic acquisition but poses a challenging problem for subsequent processing because of the interference from the neighbor crews. Separation of different sources, also called deblending, becomes important for the overall success of this acquisition technology. I propose a novel iterative estimation scheme for separating the blended simultaneous source seismic data to produce separate-source data as if they were acquired independently. I construct an augmented estimation problem, then use shaping regularization to constrain the characteristics of the model during the inversion and to obtain a suitable estimation result. The data reconstruction and source separation problems can be combined into one problem in order to make the future acquisition more flexible and efficient. In order to best utilize the capability of median filtering in attenuating spike-like noise, I also propose to use a new type of median filter (MF), termed as space-varying median filter (SVMF) to remove blending noise. SVMF can be regionally adaptive, instead of rigidly using a constant window length through the whole profile for MF. Simultaneous-source seismic data may also contain strong ambient random noise, so traditional denoising is still an important step. One of the most widely used approaches for removing random noise is using a sparse-transform thresholding strategy. I propose a double sparsity dictionary (DSD) for seismic data in order to combine the benefits of both analytic transform and learning-based dictionary. In the DSD framework, data-driven tight frame (DDTF) obtains an extra structure regularization when learning dictionaries, while the seislet transform obtains a compensation for the transformation error caused by slope dependency. DSD aims to provide a sparser representation than the individual transform and dictionary and therefore can help achieve better performance in denoising applications. Finally, considering that signal loss sometimes cannot be avoided in nearly all the existing denoising or deblending approaches. I propose a novel approach to retrieve the leakage energy from the initial noise section using local signal-and-noise orthogonalization. The proposed denoising approach corresponds to orthogonalizing the initially denoised signal and noise in a local manner. I evaluate denoising performance by using local similarity. The local signal-and-noise orthogonalization algorithm can also be used in the iterative deblending framework for obtaining better performance.