Nonstretching normal-moveout correction using a dynamic time warping algorithm

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. V27-V37 ◽  
Author(s):  
Shuangquan Chen ◽  
Song Jin ◽  
Xiang-Yang Li ◽  
Wuyang Yang
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Optimization Method ◽  
Thin Layers ◽  
Time Warping ◽  
Dynamic Matching ◽  
Seismic Waveforms ◽  
Dynamic Time

Normal-moveout (NMO) correction is one of the most important routines in seismic processing. NMO is usually implemented by a sample-by-sample procedure; unfortunately, such implementation not only decreases the frequency content 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 correction, which does not handle crossing events and convoluted events such as thin layers. Our algorithm minimizes the stretch through an automatic static temporal correction of seismic wavelets. The local static time shifts are obtained using a DTW algorithm, which is a nonlinear optimization method. To mitigate the influence of noise, we evaluated a multitrace window strategy to improve the signal-to-noise ratio of seismic data by obtaining a more precise moveout correction at far-offset traces. To illustrate the effectiveness of our algorithm, we first applied our method to synthetic data and then to field seismic data. Both tests illustrate that our algorithm minimizes the stretch associated with far offsets, and the method preserves the amplitude fidelity.

Numerical comparison of time-, frequency-, and wavelet-domain methods for coda wave interferometry

2021 ◽  
Author(s):  
Congcong Yuan ◽  
Jared Bryan ◽  
Marine Denolle
Keyword(s):  
Wavelet Transform ◽  
Dynamic Time Warping ◽  
Phase Shifts ◽  
Wavelet Domain ◽  
Time Warping ◽  
Velocity Perturbation ◽  
Seismic Waveforms ◽  
Seismic Frequencies ◽  
Velocity Changes ◽  
Dynamic Time

Summary Temporal changes in subsurface properties, such as seismic wavespeeds, can be monitored by measuring phase shifts in the coda of two seismic waveforms that share a similar source-receiver path but that are recorded at different times. These nearly identical seismic waveforms are usually obtained either from repeated earthquake waveforms or from repeated ambient noise cross-correlations. The five algorithms that are the most popular to measure phase shifts in the coda waves are the Windowed Cross Correlation (WCC), Trace Stretching (TS), Dynamic Time Warping (DTW), Moving Window Cross Spectrum (MWCS), and Wavelet Cross Spectrum (WCS). The seismic wavespeed perturbation is then obtained from the linear regression of phase shifts with their respective lag times under the assumption that the velocity perturbation is homogeneous between (virtual or active) source and receiver. We categorize these methods into the time domain (WCC, TS, DTW), frequency domain (MWCS), and wavelet domain (WCS). This study complements this suite of algorithms with two additional wavelet-domain methods, which we call Wavelet Transform Stretching (WTS) and Wavelet Transform Dynamic Time Warping (WTDTW), wherein we apply traditional stretching and dynamic time warping techniques to the wavelet transform. This work aims to verify, validate, and test the accuracy and performance of all methods by performing numerical experiments, in which the elastic wavefields are solved for in various 2D heterogeneous halfspace geometries. Through this work, we validate the assumption of a linear increase in phase shifts with respect to phase lags as a valid argument for fully homogeneous and laterally homogeneous velocity changes. Additionally, we investigate the sensitivity of coda waves at various seismic frequencies to the depth of the velocity perturbation. Overall, we conclude that seismic wavefields generated and recorded at the surface lose sensitivity rapidly with increasing depth of the velocity change for all source-receiver offsets. However, measurements made over a spectrum of seismic frequencies exhibit a pattern such that wavelet methods, and especially WTS, provide useful information to infer the depth of the velocity changes.

Seismic well tie by aligning impedance log with inverted impedance from seismic data

2020 ◽  
Vol 8 (4) ◽  
pp. T917-T925
Author(s):  
Bo Zhang ◽  
Yahua Yang ◽  
Yong Pan ◽  
Hao Wu ◽  
Danping Cao
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Real Data ◽  
Seismic Inversion ◽  
Synthetic Seismogram ◽  
Well Logs ◽  
Time Warping ◽  
Seismic Wavelet ◽  
Dynamic Time ◽  
Depth Relationship

The accuracy of seismic inversion is affected by the seismic wavelet and time-depth relationship generated by the process of the seismic well tie. The seismic well tie is implemented by comparing the synthetic seismogram computed from well logs and the poststack seismogram at or nearby the borehole location. However, precise waveform matching between the synthetic seismogram and the seismic trace does not guarantee an accurate tie between the elastic properties contained represented by the seismic data and well logs. We have performed the seismic well tie using the impedance log and the impedance inverted from poststack seismic data. We use an improved dynamic time warping to align the impedance log and impedance inverted from seismic data. Our workflow is similar to the current procedure of the seismic well tie except that the matching is implemented between the impedance log and the inverted impedance. The current seismic well-tie converges if there is no visible changes for the wavelets and time-depth relationship in the previous and current tying loops. Similarly, our seismic well tie converges if there are no visible changes for the wavelets, inverted impedance, and time-depth relationship in the previous and current tying loops. The real data example illustrates that more accurate inverted impedance is obtained by using the new wavelet and time-depth relationship.

Application of Dynamic Time Warping in Weighted Stacking of Seismic Data

2021 ◽  
pp. 1-5
Author(s):  
Chengyun Song ◽  
Lingxuan Li ◽  
Yaojun Wang ◽  
Kunhong Li ◽  
Jiying Tuo
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time

A Fault Diagnosis System Based on Bistable Stochastic Resonance and Dynamic Time Warping

2016 ◽  
Vol 693 ◽  
pp. 1294-1299 ◽  
Author(s):  
Zhen Wu Liu ◽  
Zhi Wu Shang ◽  
Ya Feng Li ◽  
Tai Yong Wang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Dynamic Time Warping ◽  
Signal To Noise Ratio ◽  
Current Signal ◽  
Time Warping ◽  
Resonance Theory ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Dynamic Time

Stator current signal of driving motor can be easily measured. Using it in the gearbox fault diagnosis system is inexpensive and suitable for remote monitoring. According to the application of the Motor Current Signal Analysis in machinery fault detection, we present a new gearbox fault diagnosis system. In modern signal processing technology, Stochastic Resonance theory is widely used to improve SNR (signal to noise ratio). Dynamic time warping algorithm is a simple and efficient way of the pattern identified. Combine the Stochastic Resonance theory and dynamic time warping algorithm as the basic theory of fault diagnosis. To realize the development of fault diagnosis software, we use the mixed-programming of MATLAB algorithms library and VC++.

Dynamic warping of seismic images

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. S105-S115 ◽  
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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