Wenkai Lu

Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach.

Independent component analysis (ICA) proves to be effective in the removing the ocular artifact from electroencephalogram recordings (EEG). While using ICA in ocular artifact correction, a crucial step is to correctly identify the artifact components among the decomposed independent components. In most previous works, this step of selecting the artifact components was manually implemented, which is time consuming and inconvenient when dealing with a large amount of EEG data. We present a new method which automatically selects the eye blink artifact components based on the pattern of their scalp topographies, which can be exemplified as a template matching approach. The feasibility of using a fixed template for singling out the eye blink component after ICA decomposition was validated by an experiment in which 18 subjects among the 21 subjects involved exhibited a highly consistent pattern of eye blink scalp topographies. Since only the spatial feature is employed for singling out the eye blink component, the proposed method is very efficient and easy to implement. Objective evaluation of the real results shows that the proposed algorithm can remove the eye blink artifact from the EEG while causing little distortion to the underlying brain activities.

Adaptive multiple subtraction using independent component analysis

Surface-related multiple attenuation (SRMA) can effectively remove multiples from seismic data when other (e.g., Radon transform) methods have difficulty. SRMA generally includes two steps: multiple prediction (or multiple modeling) and adaptive multiple subtraction (AMS). In some cases, adaptive subtraction is the main challenge for the success of SRMA. Adaptive subtraction is often posed as a least-squares minimization problem that minimizes the energy difference between the original input traces and the modeled multiple traces. The minimum-output-energy approach can be implemented by either single- or multichannel matching filters. These methods assume orthogonality between the primaries and multiples, meaning that the two classes of events are uncorrelated.

Higher-order-statistics and supertrace-based coherence-estimation algorithm

This article proposes a new higher-order-statistics-based coherence-estimation algorithm, which we denote as HOSC. Unlike the traditional crosscorrelation-based C1 coherence algorithm, which sequentially estimates correlation in the inline and crossline directions and uses their geometric mean as a coherence estimate at the analysis point, our method exploits three seismic traces simultaneously to calculate a 2D slice of their normalized fourth-order moment with one zero-lag correlation and then searches for the maximum correlation point on the 2D slice as the coherence estimate. To include more seismic traces in the coherence estimation, we introduce a supertrace technique that constructs a new data cube by rearranging several adjacent seismic traces into a single supertrace. Combining our supertrace technique with the C1 and HOSC algorithms, we obtain two efficient coherence-estimation algorithms, which we call ST-C1 and ST-HOSC. Application results on the real data set show that our algorithms are able...

Adaptive noise attenuation of seismic images based on singular value decomposition and texture direction detection

Singular value decomposition (SVD) is an efficient tool for the separation of signal and noise subspaces. When it is used to process seismic images, SVD can enhance the signal-to-noise ratio (SNR) of horizontal events effectively. In this paper, an adaptive SVD filter is proposed to enhance the non-horizontal events by detection of seismic image texture direction and then horizontal alignment of the estimated dip through data rotation. The features derived from the co-occurrence matrix are used to estimate the texture direction. The SVD filter parameter is adapted according to the ratio of the stacking energy along the detected direction and the energy of the image. Coherent noise events are recognized by their directions, which are different from the directions of signal events in general, and are first attenuated by high-rank approximation. Then, the signal events are enhanced by low-rank approximation.

Deconvolutive Short-Time Fourier Transform Spectrogram

The short-time Fourier transform (STFT) spectrogram, which is the squared modulus of the STFT, is a smoothed version of the Wigner-Ville distribution (WVD). The STFT spectrogram is 2-D convolution of the the signal WVD and the window function WVD. In this letter, we propose a deconvolutive short-time Fourier transform (DSTFT) spectrogram method, which improves the time-frequency resolution and reduces the cross-terms simultaneously by applying a 2-D deconvolution operation on the STFT spectrogram. Compared to the STFT spectrogram, the spectrogram obtained by the proposed method shows a clear improvement in the time-frequency resolution. Computer simulations are provided to illustrate the good performance of the proposed method, compared with some traditional time-frequency representation (TFR) methods.

Edge-preserving polynomial fitting method to suppress random seismic noise

This paper focuses on suppressing random seismic noise while preserving signals and edges. We propose an edge-preserving polynomial fitting (EPPF) method leading to good signal and edge preservation. The EPPF method assumes that a 1D signal can be modeled by a polynomial. A series of shifted windows are used to estimate any sample in a 1D signal. After that, the window with the minimum fitting error is selected and its output is assigned as the final estimate for this sample. For a point in 2D seismic data, several 1D signals are extracted along different directions first and then are processed by the EPPF method. After that, we select the direction with a minimum fitting error and assign its output as the final estimate for this point. Applications with synthetic and real data sets show that the EPPF method suppresses the random seismic noise effectively while preserving the signals and edges. Comparisons of results obtained by the EPPF method, the edge-preserving smoothing (EPS) method, and the polynomi...

Adaptive algebraic reconstruction technique

Algebraic reconstruction techniques (ART) are iterative procedures for reconstructing objects from their projections. It is proven that ART can be computationally efficient by carefully arranging the order in which the collected data are accessed during the reconstruction procedure and adaptively adjusting the relaxation parameters. In this paper, an adaptive algebraic reconstruction technique (AART), which adopts the same projection access scheme in multilevel scheme algebraic reconstruction technique (MLS-ART), is proposed. By introducing adaptive adjustment of the relaxation parameters during the reconstruction procedure, one-iteration AART can produce reconstructions with better quality, in comparison with one-iteration MLS-ART. Furthermore, AART outperforms MLS-ART with improved computational efficiency.

Dip-scanning coherence algorithm using eigenstructure analysis and supertrace technique

The eigenstructure-based coherence algorithms are robust to noise and able to produce enhanced coherence images. However, the original eigenstructure coherence algorithm does not implement dip scanning; therefore, it produces less satisfactory results in areas with strong structural dips. The supertrace technique also improves the coherence algorithms’ robustness by concatenating multiple seismic traces to form a supertrace. In addition, the supertrace data cube preserves the structural-dip information that is contained in the original seismic data cube; thus, dip scanning can be performed effectively using a number of adjacent supertraces. We combine the eigenstructure analysis and the dip-scanning supertrace technique to obtain a new coherence-estimation algorithm. Application to the real data set shows that the new algorithm provides good coherence estimates in areas with strong structural dips. Furthermore, the algorithm is computationally efficient because of the small covariance matrix (4×4) used fo...

Adaptive multiple subtraction based on constrained independent component analysis

Adaptive multiple subtraction is a critical and challenging procedure for the widely used surface-related multiple elimination techniques. One of the problems encountered in this procedure is that a good result usually is hard to obtain when primaries and multiples have overlap or when the true and predicted multiples have mismatches, such as wavelet difference, time shift, and scalar inconsistency. We propose an adaptive multiple subtraction method based on constrained independent component analysis (CICA). It combines the advantages of two current adaptive multiple subtraction methods: the independent component analysis method and the multidimensional prediction error filters (PEF) method. In CICA, the prediction error obtained by the PEFs, which measures the lateral continuity of the primaries, is adopted as a constraint term of its objective function.

Local linear coherent noise attenuation based on local polynomial approximation

We propose a new technique for the attenuation of locally coherent noise. We assume that the moveout of the noise is locally linear and approximate its amplitude variations with offset using piecewise (local) polynomial models. Thus, our method consists of three steps: detection of the noise (locally linear coherent noise, LLCN), amplitude estimation by a local polynomial approximation (LPA), and subtraction of the estimated coherent noise from the original data. Applying the proposed method to synthetic data and to a field data set shows that the LPA filter has good ability to model LLCN and is insensitive to the filter parameters. Comparisons of the results obtained by our method with those from the traditional frequency-wavenumber filter and the localized 2D filter in the Fourier projection domain (FPF) show that the new method outperforms both traditional methods in situations with complex coherent noise.