Empirical mode decomposition for seismic time-frequency analysis

Han, Jianxun; Baan, Mirko van der

doi:10.1190/geo2012-0199.1

Cited by 194 publications

(75 citation statements)

References 34 publications

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“…This paper is not going to cover the basic principles of HHT which have been introduced in much of the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. We will focus only on mode mixing problems of the EMD method in HHT.…”

Section: Mode Mixing Problemmentioning

confidence: 99%

See 1 more Smart Citation

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Huang

Malekian

et al. 2017

Energies

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Hilbert-Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic attributes using complementary ensemble empirical mode decomposition (CEEMD). Numerical simulation testing indicates that the CEEMD can effectively solve the mode mixing problems of EMD and can provide stronger anti-noise ability. The decomposed results of the synthetic seismic record show that CEEMD has a better ability to decompose seismic signals. Then, CEEMD is applied to extract instantaneous seismic attributes of 3D seismic data in a real-world coal mine in Inner Mongolia, China. The detection results demonstrate that instantaneous seismic attributes extracted by CEEMD are helpful to effectively identify the undulations of the top interfaces of limestone.

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Section: Mode Mixing Problemmentioning

confidence: 99%

“…Zhou et al demonstrated the new insights of EMD and HHT which can be used with seismic reflection data [13]. Xue et al used the three EMD-based analysis methods to perform a comparative study on hydrocarbon detection [14]. Han et al used EMD for seismic time-frequency analysis [15].…”

Section: Introductionmentioning

confidence: 99%

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Huang

Malekian

et al. 2017

Energies

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“…The EMD based approach is widely used in audio signal processing [12,13], computational neuroscience [14], climate signal analysis [15], image processing [16], seismic signal [17] and biomedical signal processing [18]. This study focuses on the application of EMD in advanced speech signal processing including fundamental frequency estimation [19], voiced/unvoiced speech classification [20] and speech enhancement [21,22].…”

Section: Empirical Mode Decomposition (Emd) Is Newlymentioning

confidence: 99%

Empirical Mode Decomposition for Advanced Speech Signal Processing

Molla

Das

Hamid

et al. 2013

Journal of Signal Processing

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Empirical mode decomposition (EMD) is a newly developed tool to analyze nonlinear and non-stationary signals.It is used to decompose any signal into a finite number of time varying subband signals termed as intrinsic mode functions (IMFs). Such data adaptive decomposition is recently used in speech enhancement. This study presents the concept of EMD and its application to advanced speech signal processing paradigms including speech enhancement by soft-thresholding, voiced/unvoiced (V/Uv) speech discrimination and pitch estimation. The speech processing is frequently performed in the transformed domain and the transformation is usually achieved by traditional signal analysis techniques i.e. Fourier and wavelet transformations. These analysis methods employ priori basis function and it is not suitable for data adaptive analysis for non-stationary signal like speech. Recently, EMD is taken much attention for speech signal processing in data adaptive way. Several EMD based potential soft-thresholding algorithms for speech enhancement are discussed here. The V/Uv discrimination is an important concern in speech processing. It is usually performed by using acoustic features. The training data is used to determine the threshold for classification. The EMD based data adaptive thresholding approach is developed for V/Uv discrimination without any training phase. Noticeable improvement is achieved with the application of EMD in pitch estimation of noisy speech signals. The related experimental results are also presented to realize the effectiveness of EMD in advanced speech processing algorithms.

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“…In oceanography, a combination of baroclinic modes helps model density profiles of seasonal cycles and other geophysical phenomena such as thermal or solar variation [23,64]. Similarly, in seismology, modes with differing frequency components help highlight different geological and stratigraphic information [35]. In holography, mode decomposition allows reducing speckle [46].…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Compact Variational Mode Decomposition

et al. 2017

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Decomposing multidimensional signals, such as images, into spatially compact, potentially overlapping modes of essentially wavelike nature makes these components accessible for further downstream analysis. This decomposition enables space-frequency analysis, demodulation, estimation of local orientation, edge and corner detection, texture analysis, denoising, inpainting, or curvature estimation. Our model decomposes the input signal into modes with narrow Fourier bandwidth; to cope with sharp region boundaries, incompatible with narrow bandwidth, we introduce binary support functions that act as masks on the narrow-band mode for image recomposition. L 1 and TV terms promote sparsity and spatial compactness. Constraining the support functions to partitions of the signal domain, we effectively get an image segmentation model based on spectral homogeneity. By coupling several submodes together with a single support function, we are able to decompose an image into several crystal grains. Our efficient algorithm is based on variable splitting and alternate direction optimization; we employ Merriman-Bence-Osher-like threshold dynamics to handle efficiently the motion by mean curvature of the support function boundaries under the sparsity promoting terms. The versatility and effectiveness of our proposed model is demonstrated on a broad variety of example images from different modalities. These demonstrations include the decomposition of images into overlapping modes with smooth or sharp boundaries, segmentation of images of crystal grains, and inpainting of damaged image regions through artifact detection.

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Empirical mode decomposition for seismic time-frequency analysis

Cited by 194 publications

References 34 publications

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Empirical Mode Decomposition for Advanced Speech Signal Processing

Two-Dimensional Compact Variational Mode Decomposition

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