Browsing by Author "Steeghs, Philippe"

Now showing 1 - 8 of 8
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    3-D Local Radon Power Spectra for Seismic Attribute Extraction
    (1998-01-15) Steeghs, Philippe; Fokkema, Jacob T; Diephuis, Gerhard; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper we discuss a method for volume attribute extraction that is based on a new type of local Radon power spectrum. The new algorithm results in robust and geologically meaningful volume attributes, such as volume dip and azimuth. Seismic volume attribute analysis greatly facilitates the interpretation of large 3-D seismic data volumes. However, horizon attribute maps are generally more easy to interpret than volume attribute images, which are usually time slices or cross-sections. We show that, for dip estimation, the volume attribute image is very similar to the horizon dip map.
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    Decomposition of seismic signals via time-frequency representations
    (1996-01-15) Tobback, Tom; Steeghs, Philippe; Drijkoningen, Guy G; Fokkema, Jacob T; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper we discuss the use of a time-frequency representation, the Wigner distribution, for the decomposition and characterization of seismic signals. The advantage of the Wigner distribution over other representations, such as the wavelet and sliding window Fourier transform, is its sharp localization properties in the time-frequency plane. However, the Wigner distribution is a not a linear transformation. This non-linearity complicates the use of the Wigner distribution for time-frequency filtering and decomposition. We present an optimization method for the reconstruction of a time signal from its Wigner distribution. The reconstruction technique enables a decomposition of a signal into its time-frequency components, where the reconstructed components are stripped off from the signal one by one. The method is illustrated a real data example. We also demonstrate how the decomposition can be used for suppression and enhancement of events in the time-frequency plane.
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    Fast computation of the sliding-window Radon transform with application to attribute extraction
    (1999-01-15) Steeghs, Philippe; Digital Signal Processing (http://dsp.rice.edu/)
    We have developed a fast and robust algorithm for the extraction of 3-D seismic attributes. The algorithm is based on the recursive computation of the 3-D sliding-window Radon transform. Considerable gain in computational efficiency has been achieved with respect to existing algorithms. Field data results demonstrate the effectiveness of the method for 3-D seismic interpretation.
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    Hybrid Linear/Quadratic Time-Frequency Attributes
    (2000-06-01) Baraniuk, Richard G.; Coates, Mark J.; Steeghs, Philippe; Digital Signal Processing (http://dsp.rice.edu/)
    We present an efficient method for robustly calculating time-frequency attributes of a signal, including instantaneous mean frequency, bandwidth, and kurtosis. Most current approaches involve a costly intermediate step of computing a (highly oversampled) 2-D bilinear time-frequency representation (TFR), which is then collapsed to the 1-D attribute. Using the principles of hybrid linear/bilinear time-frequency analysis, we propose computing attributes as nonlinear combinations of the (barely oversampled) linear Gabor transform of the signal. The method is both computationally efficient and accurate-it performs as well as the best bilinear techniques based on adaptive TFRs. To illustrate, we calculate an attribute of a seismic cross-section.
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    Hybrid Linear/Quadratic Time-Frequency Attributes
    (2001-04-01) Baraniuk, Richard G.; Coates, Mark J.; Steeghs, Philippe; Digital Signal Processing (http://dsp.rice.edu/)
    We present an efficient method for robustly calculating time-frequency attributes of a signal, including instantaneous mean frequency, bandwidth, kurtosis, and other moments. Most current attribute estimation techniques involve a costly intermediate step of computing a (highly oversampled) two-dimensonal (2-D) quadratic time-frequency representation (TFR), which is then collapsed to the one-dimensonal (1-D) attribute. Using the principles of hybrid linear/quadratic time-frequency analysis (time-frequency distribution series), we propose computing attributes as nonlinear combinations of the (slightly oversampled) linear Gabor coefficients of the signal. The method is both computationally efficient and accurate; it performs as well as the best techniques based on adaptive TFRs. To illustrate, we calculate an attribute of a seismic cross section.
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    Multiscale Texture Segmentation of Dip-cube Slices using Wavelet-domain Hidden Markov Trees
    (1999-11-01) Magrin-Chagnolleau, Ivan; Choi, Hyeokho; van Spaendonck, Rutger; Steeghs, Philippe; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    Wavelet-domain Hidden Markov Models (HMMs) are powerful tools for modeling the statistical properties of wavelet coefficients. By characterizing the joint statistics of wavelet coefficients, HMMs efficiently capture the characteristics of many real-world signals. When applied to images, the model can characterize the joint statistics between pixels, providing a very good classifier for textures. Utilizing the inherent tree structure of wavelet-domain HMM, classification of textures at various scales is possible, furnishing a natural tool for multiscale texture segmentation. In this paper, we introduce a new multiscale texture segmentation algorithm based on wavelet-domain HMM. Based on the multiscale classification results obtained from the wavelet-domain HMM, we develop a method to combine the multiscale classification results to generate a reliable segmentation of the texture images. We apply this new technique to the segmentation of dip-cube slices.
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    Time Frequency Analysis Applications in Geophysics
    (CRC Press, 2002-01-15) Steeghs, Philippe; Baraniuk, Richard G.; Odegard, Jan E.; Antonia Papandreou-Suppappola; Digital Signal Processing (http://dsp.rice.edu/)
    In this chapter, we overview a number of applications of time-frequency representations in seismic data processing, from the analysis of seismic sequences to efficient attribute extraction to 3-D attributes for volumetric data.
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    Time-frequency analysis of seismic sequences
    (1995-01-15) Steeghs, Philippe; Drijkoningen, Guy G; Digital Signal Processing (http://dsp.rice.edu/)
    Reflection patterns play an important role in seismic sequence stratigraphy, therefore making their quantitative description essential for the construction and validation of sequence stratigraphic models from seismic data. The characteristics of a seismic reflection pattern can be elicited from the data by representing the data as a joint function of time and frequency. Developments in the field of time-frequency analysis have led to t-f representations with better resolution than can be obtained with classical methods for local frequency analysis. This justifies a study of the application of these new representations to the analysis of seismic data. Some examples of the t-f representation of the seismic response of layered sequences are given. They clearly show the contribution of the stratigraphic sequence to the spectral content of the data. The construction of a sequence stratigraphic model from a t-f representation is demonstrated with a field data example where we match a pattern that was observed in the data to a sequence model.