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dynamic time warping

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Journal Article
Journal: Geophysics
Published: 21 August 2014
Geophysics (2014) 79 (5): R209–R220.
... from cycle skipping in the face of large time shifts. I, therefore, used the dynamic time warping (DTW) algorithm as an alternative method to estimate time shifts. I claimed the time shift estimates produced by DTW are of higher quality than those produced by the previous methods, especially...
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Journal Article
Journal: Geophysics
Published: 03 April 2025
Geophysics (2025) L85–L98.
... conditions, have been commonly used to enhance the positioning resolution. Instead of modifying the imaging conditions, we develop a dynamic time warping-based crosscorrelation method aimed at enhancing the time resolution of the crosscorrelation records. This innovative approach uses the Pearson correlation...
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Journal Article
Journal: Geophysics
Published: 10 January 2025
Geophysics (2025) 90 (2): V67–V81.
... these problems, a new multiscale dynamic time-warping (MSD) algorithm for velocity-independent stretch-free NMO correction is investigated. This algorithm has higher accuracy and stronger noise resistance, as our comparisons with other applications of dynamic time warping show. In our algorithm, the original...
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Journal Article
Journal: Geophysics
Published: 02 October 2023
Geophysics (2023) 88 (6): R737–R749.
...Jianhua Wang; Liangguo Dong; Chao Huang; Yilin Wang ABSTRACT Crosscorrelation and dynamic time warping (DTW) are ubiquitous in time-shift estimation. However, the small-shift limitation of crosscorrelation and the instability and high sensitivity to noise of DTW seriously hinder their applications...
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Journal Article
Published: 01 September 2022
Bulletin of the Seismological Society of America (2022) 112 (6): 2915–2932.
...Jiayan Tan; Charles A. Langston ABSTRACT We illustrate shape dynamic time warping (ShapeDTW) for 1D structure waveform inversion with examples from Tanzania, East Africa, and Northeastern Oklahoma. Seismograms of the M w 5.9 Lake Rukwa, Tanzania, earthquake (18 August 1994) recorded by regional...
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Journal Article
Journal: Geophysics
Published: 06 May 2022
Geophysics (2022) 87 (4): R325–R335.
...Fuqiang Chen; Daniel Peter; Matteo Ravasi ABSTRACT The dynamic time warping (DTW) misfit function has been used for full-waveform inversion to mitigate the local minima issue. However, it is not a smooth measurement and can yield strong discontinuities in the adjoint source. Such a weakness hampers...
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Journal Article
Published: 28 March 2022
Seismological Research Letters (2022) 93 (3): 1909–1921.
...Utpal Kumar; Cédric. P. Legendre; Li Zhao; Ben F. Chao Abstract We investigate the feasibility of using the dynamic time warping (DTW) technique as an alternative to windowed cross correlation (WCC) for an indirect measure to quantify both the similarity and relationship between two seismic time...
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Journal Article
Journal: Geophysics
Published: 24 January 2022
Geophysics (2022) 87 (2): R223–R230.
... estimate the accurate velocities from well logs, with which FWI can converge to a globally optimal model without needing very low frequencies. We propose using a dynamic time warping (DTW) localized rewarp method and a convolutional neural network (CNN) method alternatively to build the starting model...
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Journal Article
Journal: Geophysics
Published: 23 July 2020
Geophysics (2020) 85 (5): O71–O82.
...Luke Decker; Qunshan Zhang ABSTRACT 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...
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Journal Article
Journal: Geophysics
Published: 29 November 2017
Geophysics (2018) 83 (1): V27–V37.
... but also distorts the amplitude of seismic waveforms resulting from the well-known stretch. The degree of stretch increases with increasing offset. To minimize severe stretch associated with far offset, we use a dynamic time warping (DTW) algorithm to achieve an automatic dynamic matching NMO nonstretch...
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Series: Geological Society, London, Special Publications
Published: 01 January 2015
DOI: 10.1144/SP414.14
EISBN: 9781862397156
... the dynamic time warping (DTW) algorithm, were constrained by upgraded conodont biozonation across the Lau Event interval (LEI). Two sections (Pozary and Muslovka) representing shallow-water facies in the Prague Synform were studied. Conodont data and DTW alignment show that Muslovka represents a less...
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Image
Distance matrices obtained using dynamic time warping (DTW) and cross‐correlation (CC*) dissimilarities. (a) DTW distance matrix. (b) CC* distance matrix. Periods were divided visually and numbered following Table 2. Blue depicts similarity and white or red dissimilarity, and values are depicted following the color bar at the bottom. Black lines delimit same‐period dissimilarity portions. The color version of this figure is available only in the electronic edition.
Published: 16 October 2024
Figure 7. Distance matrices obtained using dynamic time warping (DTW) and cross‐correlation (CC*) dissimilarities. (a) DTW distance matrix. (b) CC* distance matrix. Periods were divided visually and numbered following Table  2 . Blue depicts similarity and white or red dissimilarity, and values
Image
Published: 18 June 2024
TABLE 2. DYNAMIC TIME WARPING CORRELATION SIGNIFICANCE
Image
The alignment procedure of dynamic time warping (DTW). (a) Shows original waveforms, and the corresponding data points are connected by DTW (black lines). (b) Indicates two warped waveforms generated by DTW. Their data points are aligned one‐by‐one. (c) Plots the distance matrix of two waveforms. Colors in the matrix represent Euclidean distances of the samples from two waveforms. (d) Plots the cumulative distance matrix, which is updated from the distance matrix. The optimal warping paths are represented by yellow circles. The color version of this figure is available only in the electronic edition.
Published: 01 September 2022
Figure 2. The alignment procedure of dynamic time warping (DTW). (a) Shows original waveforms, and the corresponding data points are connected by DTW (black lines). (b) Indicates two warped waveforms generated by DTW. Their data points are aligned one‐by‐one. (c) Plots the distance matrix of two
Image
A comparison example between DTW and shape dynamic time warping (ShapeDTW) with different shape descriptors. (a) Conventional DTW, (b) ShapeDTW with the shape descriptors, which are formed by raw subsequences, (c) the means of descriptors in (b) are removed from these shape descriptors, (d) ShapeDTW with the compound shape descriptors, which are linearly summed by the descriptors in (b) and (c). Warped paths are represented by yellow circles in these matrices. The lengths of warped waveforms are labeled in waveform windows. The color version of this figure is available only in the electronic edition.
Published: 01 September 2022
Figure 5. A comparison example between DTW and shape dynamic time warping (ShapeDTW) with different shape descriptors. (a) Conventional DTW, (b) ShapeDTW with the shape descriptors, which are formed by raw subsequences, (c) the means of descriptors in (b) are removed from these shape descriptors
Image
A comparison example between DTW and shape dynamic time warping (ShapeDTW) with different shape descriptors. (a) Conventional DTW, (b) ShapeDTW with the shape descriptors, which are formed by raw subsequences, (c) the means of descriptors in (b) are removed from these shape descriptors, (d) ShapeDTW with the compound shape descriptors, which are linearly summed by the descriptors in (b) and (c). Warped paths are represented by yellow circles in these matrices. The lengths of warped waveforms are labeled in waveform windows. The color version of this figure is available only in the electronic edition.
Published: 01 September 2022
Figure 5. A comparison example between DTW and shape dynamic time warping (ShapeDTW) with different shape descriptors. (a) Conventional DTW, (b) ShapeDTW with the shape descriptors, which are formed by raw subsequences, (c) the means of descriptors in (b) are removed from these shape descriptors
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Dynamic time warping (DTW) of Kominz et al. (2008) eustatic curve and deep-water sand delivery rate. DTW is a method where the optimal alignment between two time series is calculated. Detrended and normalized time series are stretched (warped) nonlinearly along the time axis to minimize the distance between the time series. Note how the gray correlation lines are bent down, straight, or bent up. This highlights a lead (bent down) or lag (bent up) of deep-water sand peaks with respect to sea-level minima. This demonstrates that peaks in deep-water sand delivery can occur in any part of a sea-level fall.
Published: 01 December 2016
Fig. 7.— Dynamic time warping (DTW) of Kominz et al. (2008) eustatic curve and deep-water sand delivery rate. DTW is a method where the optimal alignment between two time series is calculated. Detrended and normalized time series are stretched (warped) nonlinearly along the time axis
Image
Dynamic Time Warping (DTW) plots comparing ecologic stress cluster successions. The white line extending from the bottom left corner to the top right corner is the optimal warp path (OWP). The more diagonal the OWP, the more similar the two successions compared. Horizontal and vertical deviations highlight intervals of no match between successions. The grayscale spectrum corresponds to the contoured accumulated cost matrix. The OWP selects the path with the lowest accumulated cost. A) DTW plot of 12-11-098-11W4 and 14-13-098-11W4. The OWP is relatively diagonal suggesting strong similarity between the two successions. B) DTW plot of 12-11-098-11W4 and 13-02-098-11W4. The OWP is broadly diagonal suggesting strong similarity between the two successions. C) DTW plot of 14-13-098-11W4 and 13-02-098-11W4. The OWP is still relatively diagonal, but much less than 7a, 7b. This suggests some degree of similarity. D) DTW plot of 13-02-098-11W4 and 11-17-098-10W4. The OWP is significantly deviated from the diagonal for a large portion of the plot, suggesting limited similarity. The portion from approximately 25 to 200 on the 13-02-098-11W4 shows some degree of similarity. E) DTW plot of 12-11-098-11W4 and 11-17-098-10W4. Overall, the OWP strays strongly from the diagonal, which suggests relatively little similarity between the two successions. F) DTW plot between 14-13-098-11W4 and 11-17-098-10W4. The OWP strays from the diagonal, which suggests relatively little similarity between the two successions.
Published: 01 November 2016
Fig. 9.— Dynamic Time Warping (DTW) plots comparing ecologic stress cluster successions. The white line extending from the bottom left corner to the top right corner is the optimal warp path (OWP). The more diagonal the OWP, the more similar the two successions compared. Horizontal and vertical
Journal Article
Journal: PALAIOS
Published: 01 November 2016
PALAIOS (2016) 31 (11): 533–548.
...Fig. 9.— Dynamic Time Warping (DTW) plots comparing ecologic stress cluster successions. The white line extending from the bottom left corner to the top right corner is the optimal warp path (OWP). The more diagonal the OWP, the more similar the two successions compared. Horizontal and vertical...
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Journal Article
Journal: Interpretation
Published: 06 July 2017
Interpretation (2017) 5 (3): SJ81–SJ90.
..., inaccurate. Automatic methods, such as dynamic time warping (DTW), can match synthetic traces to seismic data. Although these methods are extremely fast, they tend to create interval velocities that are not geologically realistic. We have described the modification of DTW to create a blocked dynamic warping...
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