Quantifying and correcting residual azimuthal anisotropic moveout in image gathers using dynamic time warping

We have developed a novel application of dynamic time warping (DTW) for correcting residual moveout in image gathers, enhancing seismic images, and determining azimuthal anisotropic orientation and relative intensity when moveout is caused by wave propagation through a medium possessing elliptical horizontally transverse isotropy (HTI). The method functions by first using DTW to determine the sequences of integer shifts that most closely match seismic traces within an image gather to its stack and then applying those shifts to flatten the gather. Flattening shifts are fitted to an ellipse to provide an approximation for the orientation and relative strength of elliptical HTI anisotropy. We evaluated the method on synthetic and 3D field data examples to show how it is able to (1) correct for residual azimuthal anisotropic moveout, (2) accurately recover high-frequency information and improve feature resolution in seismic images, and (3) determine the anisotropic orientation while providing a measure of relative strength of elliptic anisotropy. Although the method is not intended to replace anisotropic processing techniques for moveout correction, we find that it has the ability to inexpensively approximate the effects of such operations while providing a representation of the elliptic HTI anisotropy present within a volume.

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Weld Detect Identification Using Texture Features and Dynamic Time Warping

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.752-753.1045 ◽

2015 ◽

Vol 752-753 ◽

pp. 1045-1050

Author(s):

Haniza Yazid ◽

Hamzah Arof ◽

Hafizal Yazid ◽

Norazian Abd Razak

Keyword(s):

Dynamic Time Warping ◽

Region Of Interest ◽

Texture Features ◽

Extraction Process ◽

Time Warping ◽

Image Processing Techniques ◽

Otsu Thresholding ◽

Fcm Clustering ◽

Dynamic Time ◽

Processing Techniques

In this paper, a simple yet robust algorithm for texture identification using 1 Dimensional Discrete Fourier Transform (1-D DFT) and Dynamic Time Warping (DTW) is presented with illumination variations. In the first stage, several image processing techniques namely Fuzzy C means (FCM) clustering, edge detection, Otsu thresholding and inverse surface thresholding method are utilized to locate the region of interest (ROI) where defects might exist. Next, the image undergoes the feature extraction process using 1-D DFT and finally, the features are classified using DTW. Several defect images consist of 2 types of defect namely the porosity and crack are experimented and classified using the DTW.

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Time Delay Estimation on COREX Parameters Based on Dynamic Time Warping Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.1168 ◽

2012 ◽

Vol 241-244 ◽

pp. 1168-1175

Author(s):

Chen Xi Zhao ◽

Jin Wu Xu ◽

Min Li ◽

Jian Hong Yang

Keyword(s):

Time Delay ◽

Quality Indicators ◽

Process Parameters ◽

Dynamic Time Warping ◽

Field Data ◽

Time Delay Estimation ◽

Delay Estimation ◽

Real Field ◽

Time Warping ◽

Dynamic Time

In order to solve the time delay problem between the process parameters and the quality indicators in the modeling processes, a method of time delay estimation on COREX parameters is proposed based on Dynamic Time Warping (DTW) algorithm. The method solves the problem existing in the conventional methods which demand the number of calculating sample to be same. Taking the real field data from Baosteel COREX-3000 as the research object, the DTW distances between the process parameters and the quality indicators are calculated, and then the delay time is estimated. The real field data are used for verification, the results show that the proposed method can estimate the time daley effectively, and the prediction accuracy of model which used time delay estimation becomes higher. It provides an effective measure for model preprocessing.

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Dynamic warping of seismic images

Geophysics ◽

10.1190/geo2012-0327.1 ◽

2013 ◽

Vol 78 (2) ◽

pp. S105-S115 ◽

Cited By ~ 153

Author(s):

Dave Hale

Keyword(s):

Seismic Data ◽

Dynamic Time Warping ◽

Image Warping ◽

Seismic Data Processing ◽

Time Warping ◽

Relative Time ◽

Dynamic Time ◽

Alignment Errors ◽

Seismic Reflectors ◽

Seismic Images

The problem of estimating relative time (or depth) shifts between two seismic images is ubiquitous in seismic data processing. This problem is especially difficult where shifts are large and vary rapidly with time and space, and where images are contaminated with noise or for other reasons are not shifted versions of one another. A new solution to this problem requires only simple extensions of a classic dynamic time warping algorithm for speech recognition. A key component of that classic algorithm is a nonlinear accumulation of alignment errors. By applying the same nonlinear accumulator repeatedly in all directions along all sampled axes of a multidimensional image, I obtain a new and effective method for dynamic image warping (DIW). In tests where known shifts vary rapidly, this new method is more accurate than methods based on crosscorrelations of windowed images. DIW also aligns seismic reflectors well in examples where shifts are unknown, for images with differences not limited to time shifts.

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