Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion
The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13014 |
| _version_ | 1827575863259955200 |
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| author | Qiyuan Qi Wensha Huang Donghao Zhang Liguo Han |
| author_facet | Qiyuan Qi Wensha Huang Donghao Zhang Liguo Han |
| author_sort | Qiyuan Qi |
| collection | DOAJ |
| description | The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method. |
| first_indexed | 2024-03-08T21:02:59Z |
| format | Article |
| id | doaj.art-d5f0e1a10587436cb991cf98343db8ec |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:02:59Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d5f0e1a10587436cb991cf98343db8ec2023-12-22T13:50:17ZengMDPI AGApplied Sciences2076-34172023-12-0113241301410.3390/app132413014Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet InversionQiyuan Qi0Wensha Huang1Donghao Zhang2Liguo Han3School of Economics and Management, Jilin Engineering Normal University, Changchun 130052, ChinaCollege of GeoExploration Science and Technology, Jilin University, Changchun 130021, ChinaCollege of GeoExploration Science and Technology, Jilin University, Changchun 130021, ChinaCollege of GeoExploration Science and Technology, Jilin University, Changchun 130021, ChinaThe elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method.https://www.mdpi.com/2076-3417/13/24/13014elastic full-waveform inversionsource waveletcross-correlation norm |
| spellingShingle | Qiyuan Qi Wensha Huang Donghao Zhang Liguo Han Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion Applied Sciences elastic full-waveform inversion source wavelet cross-correlation norm |
| title | Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion |
| title_full | Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion |
| title_fullStr | Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion |
| title_full_unstemmed | Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion |
| title_short | Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion |
| title_sort | robust elastic full waveform inversion based on normalized cross correlation source wavelet inversion |
| topic | elastic full-waveform inversion source wavelet cross-correlation norm |
| url | https://www.mdpi.com/2076-3417/13/24/13014 |
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