The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

Hao Wu - One of the best experts on this subject based on the ideXlab platform.

  • seismic time frequency analysis via adaptive Mode Separation based wavelet transform
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Fangyu Li, Bangyu Wu, Ying Hu, Hao Wu
    Abstract:

    To better reveal time-varying spectral components of nonstationary seismic signals, time–frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral Mode Separation and an adaptive wavelet bank design. The proposed adaptive Mode Separation-based wavelet transform (AMSWT) generates a superior time–frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral Modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical Mode decomposition (EMD), variational Mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

  • Seismic Time–Frequency Analysis via Adaptive Mode Separation-Based Wavelet Transform
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Fangyu Li, Bangyu Wu, Ying Hu, Hao Wu
    Abstract:

    To better reveal time-varying spectral components of nonstationary seismic signals, time-frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral Mode Separation and an adaptive wavelet bank design. The proposed adaptive Mode Separation-based wavelet transform (AMSWT) generates a superior time-frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral Modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical Mode decomposition (EMD), variational Mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

Fangyu Li - One of the best experts on this subject based on the ideXlab platform.

  • seismic time frequency analysis via adaptive Mode Separation based wavelet transform
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Fangyu Li, Bangyu Wu, Ying Hu, Hao Wu
    Abstract:

    To better reveal time-varying spectral components of nonstationary seismic signals, time–frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral Mode Separation and an adaptive wavelet bank design. The proposed adaptive Mode Separation-based wavelet transform (AMSWT) generates a superior time–frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral Modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical Mode decomposition (EMD), variational Mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

  • Seismic Time–Frequency Analysis via Adaptive Mode Separation-Based Wavelet Transform
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Fangyu Li, Bangyu Wu, Ying Hu, Hao Wu
    Abstract:

    To better reveal time-varying spectral components of nonstationary seismic signals, time-frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral Mode Separation and an adaptive wavelet bank design. The proposed adaptive Mode Separation-based wavelet transform (AMSWT) generates a superior time-frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral Modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical Mode decomposition (EMD), variational Mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

Bangyu Wu - One of the best experts on this subject based on the ideXlab platform.

  • seismic time frequency analysis via adaptive Mode Separation based wavelet transform
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Fangyu Li, Bangyu Wu, Ying Hu, Hao Wu
    Abstract:

    To better reveal time-varying spectral components of nonstationary seismic signals, time–frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral Mode Separation and an adaptive wavelet bank design. The proposed adaptive Mode Separation-based wavelet transform (AMSWT) generates a superior time–frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral Modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical Mode decomposition (EMD), variational Mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

  • Seismic Time–Frequency Analysis via Adaptive Mode Separation-Based Wavelet Transform
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Fangyu Li, Bangyu Wu, Ying Hu, Hao Wu
    Abstract:

    To better reveal time-varying spectral components of nonstationary seismic signals, time-frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral Mode Separation and an adaptive wavelet bank design. The proposed adaptive Mode Separation-based wavelet transform (AMSWT) generates a superior time-frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral Modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical Mode decomposition (EMD), variational Mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

Ying Hu - One of the best experts on this subject based on the ideXlab platform.

  • seismic time frequency analysis via adaptive Mode Separation based wavelet transform
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Fangyu Li, Bangyu Wu, Ying Hu, Hao Wu
    Abstract:

    To better reveal time-varying spectral components of nonstationary seismic signals, time–frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral Mode Separation and an adaptive wavelet bank design. The proposed adaptive Mode Separation-based wavelet transform (AMSWT) generates a superior time–frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral Modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical Mode decomposition (EMD), variational Mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

  • Seismic Time–Frequency Analysis via Adaptive Mode Separation-Based Wavelet Transform
    IEEE Geoscience and Remote Sensing Letters, 2020
    Co-Authors: Fangyu Li, Bangyu Wu, Ying Hu, Hao Wu
    Abstract:

    To better reveal time-varying spectral components of nonstationary seismic signals, time-frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral Mode Separation and an adaptive wavelet bank design. The proposed adaptive Mode Separation-based wavelet transform (AMSWT) generates a superior time-frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral Modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical Mode decomposition (EMD), variational Mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

Paul Sava - One of the best experts on this subject based on the ideXlab platform.

  • elastic wave Mode Separation for tilted transverse isotropy media
    Geophysical Prospecting, 2012
    Co-Authors: Paul Sava
    Abstract:

    Seismic waves propagate through the earth as a superposition of different wave Modes. Seismic imaging in areas characterized by complex geology requires techniques based on accurate reconstruction of the seismic wavefields. A crucial component of the methods in this category, collectively known as wave-equation migration, is the imaging condition that extracts information about the discontinuities of physical properties from the reconstructed wavefields at every location in space. Conventional acoustic migration techniques image a scalar wavefield representing the P-wave Mode, in contrast to elastic migration techniques, which image a vector wavefield representing both the P- and S-waves. For elastic imaging, it is desirable that the reconstructed vector fields are decomposed into pure wave Modes, such that the imaging condition produces interpretable images, characterizing, for example, PP or PS reflectivity. In anisotropic media, wave Mode Separation can be achieved by projection of the reconstructed vector fields on the polarization vectors characterizing various wave Modes. For heterogeneous media, because polarization directions change with position, wave Mode Separation needs to be implemented using space-domain filters. For transversely isotropic media with a tilted symmetry axis, the polarization vectors depend on the elastic material parameters, including the tilt angles. Using these parameters, we separate the wave Modes by constructing nine filters corresponding to the nine Cartesian components of the three polarization directions at every grid point. Since the S polarization vectors in transverse isotropic media are not defined in the singular directions, e.g., along the symmetry axes, we construct these vectors by exploiting the orthogonality between the SV and SH polarization vectors, as well as their orthogonality with the P polarization vector. This procedure allows one to separate all three Modes, with better preserved P-wave amplitudes than S-wave amplitudes. Realistic synthetic examples show that this wave Mode Separation is effective for both 2D and 3D Models with strong heterogeneity and anisotropy.

  • improving the efficiency of elastic wave Mode Separation for heterogeneous tilted transverse isotropic media
    Geophysics, 2011
    Co-Authors: Paul Sava
    Abstract:

    Wave-Mode Separation for TI (transversely isotropic) Models can be carried out by nonstationary filtering the elastic wavefields with localized filters. These filters are constructed based on the polarization vectors obtained by solving the Christoffel equation using local medium parameters. This procedure, although accurate, is computationally expensive, especially in 3D. We develop an efficient method for wave-Mode Separation, which exploits the same general idea of projecting wavefields onto polarization vectors. The method consists of two steps: (1) separate wave Modes in the wavenumber domain at a number of reference Models to obtain the same number of partially separated wavefields; then transform all the wavefields to the space domain; (2) interpolate the wavefields (obtained in step 1) in the space domain using the spatially-variable Model parameters. The new method resembles the phaseshift plus interpolation (PSPI) technique, which interpolates the wavefields that are reconstructed at several reference velocities. Synthetic examples indicate that the Separation followed by interpolation is effective for Models with complex geology. The new technique has the benefits of speed and accuracy.

  • elastic wave Mode Separation for vti media
    Geophysics, 2009
    Co-Authors: Paul Sava
    Abstract:

    Elastic wave propagation in anisotropic media is well represented by elastic wave equations. Modeling based on elasticwaveequationscharacterizesbothkinematicsanddynamics correctly. However, because P- and S-Modes are both propagated using elastic wave equations, there is a need to separate P- and S-Modes to efficiently apply single-Mode processing tools. In isotropic media, wave Modes are usually separated using Helmholtz decomposition. However, Helmholtz decomposition using conventional divergence and curl operators in anisotropic media does not give satisfactory resultsandleavesthedifferentwaveModesonlypartiallyseparated.TheSeparationofanisotropicwavefieldsrequiresmore sophisticated operators that depend on local material parameters. Anisotropic wavefield-Separation operators are constructedusingthepolarizationvectorsevaluatedateachpoint of the medium by solving the Christoffel equation for local mediumparameters.Thesepolarizationvectorscanberepresented in the space domain as localized filtering operators, which resemble conventional derivative operators. The spatially variable pseudo-derivative operators perform well in heterogeneous VTI media even at places of rapid velocity/ densityvariation.Syntheticresultsindicatethattheoperators can be used to separate wavefields for VTI media with an arbitrarydegreeofanisotropy.