The accuracy of seismic wavelet extraction influences the accuracy of analysis and dealing for seismic data directly. In fact, the wavelet in seismic data has the characteristics of time-varying. There are many methods for wavelet extraction, but the results using existing methods are not satisfying. This paper studies a new method for the separation of seismic wavelet and reflection coefficient from seismic data using Empirical Mode Decomposition (EMD) which have the superiorities of adaptive decomposition and multi-scale analysis. Firstly, we cut the seismic data into different segmentations and regard each segmentation as stationary signals while combining the characteristics of wavelet and reflection coefficient based on the hypothesis of stationarity. Then, we do preprocessing which is an important step. After preprocessing, Mirror extension inhibit the endpoint effect. Finally, using EMD decomposes the logarithmic amplitude spectrum of each segmentation and selecting different Intrinsic Mode Functions (IMF) which are smooth and continuous restructures the wavelet. The simulation results show that this method can implement the separation of seismic wavelet and reflection coefficient precisely. This paper lay a foundation for later high-precision extraction of seismic wavelet.
| Published in | Science Discovery (Volume 5, Issue 2) |
| DOI | 10.11648/j.sd.20170502.16 |
| Page(s) | 118-128 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Time-Varying Wavelet Extraction, Non-stationary Signal, Preprocessing, Mirror Extension, EMD
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APA Style
Lu Zi-hao, Dai Yong-shou, Gao Xu, Zhang Peng, Tan Yong-cheng, et al. (2017). A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering. Science Discovery, 5(2), 118-128. https://doi.org/10.11648/j.sd.20170502.16
ACS Style
Lu Zi-hao; Dai Yong-shou; Gao Xu; Zhang Peng; Tan Yong-cheng, et al. A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering. Sci. Discov. 2017, 5(2), 118-128. doi: 10.11648/j.sd.20170502.16
AMA Style
Lu Zi-hao, Dai Yong-shou, Gao Xu, Zhang Peng, Tan Yong-cheng, et al. A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering. Sci Discov. 2017;5(2):118-128. doi: 10.11648/j.sd.20170502.16
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Download@article{10.11648/j.sd.20170502.16,
author = {Lu Zi-hao and Dai Yong-shou and Gao Xu and Zhang Peng and Tan Yong-cheng and Zhang Hong-qian},
title = {A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering},
journal = {Science Discovery},
volume = {5},
number = {2},
pages = {118-128},
doi = {10.11648/j.sd.20170502.16},
url = {https://doi.org/10.11648/j.sd.20170502.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170502.16},
abstract = {The accuracy of seismic wavelet extraction influences the accuracy of analysis and dealing for seismic data directly. In fact, the wavelet in seismic data has the characteristics of time-varying. There are many methods for wavelet extraction, but the results using existing methods are not satisfying. This paper studies a new method for the separation of seismic wavelet and reflection coefficient from seismic data using Empirical Mode Decomposition (EMD) which have the superiorities of adaptive decomposition and multi-scale analysis. Firstly, we cut the seismic data into different segmentations and regard each segmentation as stationary signals while combining the characteristics of wavelet and reflection coefficient based on the hypothesis of stationarity. Then, we do preprocessing which is an important step. After preprocessing, Mirror extension inhibit the endpoint effect. Finally, using EMD decomposes the logarithmic amplitude spectrum of each segmentation and selecting different Intrinsic Mode Functions (IMF) which are smooth and continuous restructures the wavelet. The simulation results show that this method can implement the separation of seismic wavelet and reflection coefficient precisely. This paper lay a foundation for later high-precision extraction of seismic wavelet.},
year = {2017}
}
TY - JOUR T1 - A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering AU - Lu Zi-hao AU - Dai Yong-shou AU - Gao Xu AU - Zhang Peng AU - Tan Yong-cheng AU - Zhang Hong-qian Y1 - 2017/04/20 PY - 2017 N1 - https://doi.org/10.11648/j.sd.20170502.16 DO - 10.11648/j.sd.20170502.16 T2 - Science Discovery JF - Science Discovery JO - Science Discovery SP - 118 EP - 128 PB - Science Publishing Group SN - 2331-0650 UR - https://doi.org/10.11648/j.sd.20170502.16 AB - The accuracy of seismic wavelet extraction influences the accuracy of analysis and dealing for seismic data directly. In fact, the wavelet in seismic data has the characteristics of time-varying. There are many methods for wavelet extraction, but the results using existing methods are not satisfying. This paper studies a new method for the separation of seismic wavelet and reflection coefficient from seismic data using Empirical Mode Decomposition (EMD) which have the superiorities of adaptive decomposition and multi-scale analysis. Firstly, we cut the seismic data into different segmentations and regard each segmentation as stationary signals while combining the characteristics of wavelet and reflection coefficient based on the hypothesis of stationarity. Then, we do preprocessing which is an important step. After preprocessing, Mirror extension inhibit the endpoint effect. Finally, using EMD decomposes the logarithmic amplitude spectrum of each segmentation and selecting different Intrinsic Mode Functions (IMF) which are smooth and continuous restructures the wavelet. The simulation results show that this method can implement the separation of seismic wavelet and reflection coefficient precisely. This paper lay a foundation for later high-precision extraction of seismic wavelet. VL - 5 IS - 2 ER -
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