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

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. P13-P18 ◽  
Author(s):  
Wenkai Lu ◽  
Yandong Li ◽  
Shanwen Zhang ◽  
Huanqin Xiao ◽  
Yanda Li
Keyword(s):  
Order Statistics ◽  
Geometric Mean ◽  
Real Data ◽  
Estimation Algorithm ◽  
Higher Order ◽  
Data Cube ◽  
Order Moment ◽  
Higher Order Statistics ◽  
Data Set ◽  
Coherence Estimation

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 to reveal more details about the structural and stratigraphic features than the traditional C1 algorithm, yet still preserve its computational efficiency.

Dip-scanning coherence algorithm using eigenstructure analysis and supertrace technique

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V61-V66 ◽  
Author(s):  
Yandong Li ◽  
Wenkai Lu ◽  
Huanqin Xiao ◽  
Shanwen Zhang ◽  
Yanda Li
Keyword(s):  
Covariance Matrix ◽  
Seismic Data ◽  
Real Data ◽  
Estimation Algorithm ◽  
Data Cube ◽  
Computationally Efficient ◽  
Data Set ◽  
Matrix Formula ◽  
Coherence Estimation ◽  
Robust To Noise

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 [Formula: see text] used for the eigenstructure analysis.

FEATURE EXTRACTION OF MOTOR IMAGERY EEG BASED ON WAVELET TRANSFORM AND HIGHER-ORDER STATISTICS

2010 ◽  
Vol 08 (03) ◽  
pp. 373-384 ◽  
Author(s):  
RENHUAN YANG ◽  
AIGUO SONG ◽  
BAOGUO XU
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Order Statistics ◽  
Motor Imagery ◽  
Higher Order ◽  
Higher Order Statistics ◽  
Data Set ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Harmonic Wavelet

Feature extraction plays an important role in brain-computer interface (BCI) systems. In order to characterize the motor imagery related rhythm and higher-order statistics information contained within the EEG signals, a novel feature extraction method based on harmonic wavelet transform and bispectrum is developed and applied to the recognition of right and left motor imageries for developing EEG-based BCI systems. The experimental results on the Graz BCI data set have shown that the separability of the two classes features extracted by the proposed method is notable. Its performance was evaluated by a linear discriminant analysis (LDA) classifier. The recognition accuracy of 90% was obtained. The recognition results have demonstrated the effectiveness of the proposed method. This method provides an effective way for EEG feature extraction in BCI system.

Strategy for automated analysis of passive microseismic data based on S-transform, Otsu’s thresholding, and higher order statistics

Geophysics ◽  
2012 ◽  
Vol 77 (6) ◽  
pp. KS43-KS54 ◽  
Author(s):  
G-Akis Tselentis ◽  
Nikolaos Martakis ◽  
Paraskevas Paraskevopoulos ◽  
Athanasios Lois ◽  
Efthimios Sokos
Keyword(s):  
High Resolution ◽  
Order Statistics ◽  
Single Channel ◽  
Seismic Station ◽  
Real Data ◽  
Automated Analysis ◽  
Higher Order ◽  
Higher Order Statistics ◽  
Small Magnitude ◽  
S Transform

Small-magnitude seismic events, either natural or induced microearthquakes, have increasingly been used in exploration seismology with applications ranging from hydrocarbon and geothermal reservoir exploration to high-resolution passive seismic tomography surveys. We developed an automated methodology for processing and analyzing continuously recorded, single-channel seismic data. This method comprised a chi-squared-based statistical test for microseismic event detection and denoising filtering in the S-transform domain based on the Otsu thresholding method. An automatic P-phase picker based on higher order statistics criteria was used. The method was used with data from a surface seismic station. The performance of the method was tested and evaluated on synthetic and real data from a microseismic network used in a high-resolution PST survey and revealed a high level of consistency.

Higher‐order statistics based coherency estimation algorithm

2003 ◽  
Author(s):  
Wenkai Lu ◽  
Yandong Li ◽  
Shanwen Zhang ◽  
Huanqin Xiao
Keyword(s):  
Order Statistics ◽  
Estimation Algorithm ◽  
Higher Order ◽  
Higher Order Statistics

Evaluation of a new Power Quality index, based in Higher Order Statistics

2017 ◽  
Vol 1 (15) ◽  
pp. 37-42
Author(s):  
J.M. Sierra-Fernández ◽  
J.J. González De La Rosa ◽  
A. Agüera-Pérez ◽  
J.C. Palomares Salas ◽  
O. Florencias-Oliveros
Keyword(s):  
Order Statistics ◽  
Power Quality ◽  
Quality Index ◽  
Higher Order ◽  
Higher Order Statistics
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