A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
The prediction of reservoir and caprock thickness is important in geological evaluations for site selection for aquifer underground gas storage. Therefore, high-resolution seismic identification of reservoirs and caprocks is crucial. High-resolution time–frequency decomposition is one of the key met...
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/8034 |
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| author | Yanyan Ma Haixia Kang Weifeng Luo Yunxiao Zhang Lintao Luo |
| author_facet | Yanyan Ma Haixia Kang Weifeng Luo Yunxiao Zhang Lintao Luo |
| author_sort | Yanyan Ma |
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| description | The prediction of reservoir and caprock thickness is important in geological evaluations for site selection for aquifer underground gas storage. Therefore, high-resolution seismic identification of reservoirs and caprocks is crucial. High-resolution time–frequency decomposition is one of the key methods for identifying sedimentary layers. Based on this, we propose a least squares constrained spectral analysis method using a greedy fast shrinkage algorithm. This method replaces the traditional Tikhonov regularization objective function with an L1-norm regularized objective function and employs a greedy fast shrinkage algorithm. By utilizing shorter window lengths to segment the data into more precise series, the method significantly improves the computational efficiency of spectral analysis while also enhancing its accuracy to a certain extent. Numerical models demonstrate that compared to the time–frequency spectra obtained using traditional methods such as wavelet transform, short-time Fourier transform, and generalized S-transform, the proposed method can achieve high-resolution extraction of the dominant frequencies of seismic waves, with superior noise resistance. Furthermore, its application in a research area in southern China shows that the method can effectively predict thicker sedimentary layers in low-frequency ranges and accurately identify thinner sedimentary layers in high-frequency ranges. |
| format | Article |
| id | doaj-art-5e0cc7b1d67740cb893545a06bfe7fbf |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5e0cc7b1d67740cb893545a06bfe7fbf2025-08-20T03:58:30ZengMDPI AGApplied Sciences2076-34172025-07-011514803410.3390/app15148034A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares ConstraintsYanyan Ma0Haixia Kang1Weifeng Luo2Yunxiao Zhang3Lintao Luo4Oil and Gas Survey, China Geological Survey, No. 267 North Fourth Ring Middle Road, Haidian District, Beijing 100083, ChinaOil and Gas Survey, China Geological Survey, No. 267 North Fourth Ring Middle Road, Haidian District, Beijing 100083, ChinaOil and Gas Survey, China Geological Survey, No. 267 North Fourth Ring Middle Road, Haidian District, Beijing 100083, ChinaOil and Gas Survey, China Geological Survey, No. 267 North Fourth Ring Middle Road, Haidian District, Beijing 100083, ChinaOil and Gas Survey, China Geological Survey, No. 267 North Fourth Ring Middle Road, Haidian District, Beijing 100083, ChinaThe prediction of reservoir and caprock thickness is important in geological evaluations for site selection for aquifer underground gas storage. Therefore, high-resolution seismic identification of reservoirs and caprocks is crucial. High-resolution time–frequency decomposition is one of the key methods for identifying sedimentary layers. Based on this, we propose a least squares constrained spectral analysis method using a greedy fast shrinkage algorithm. This method replaces the traditional Tikhonov regularization objective function with an L1-norm regularized objective function and employs a greedy fast shrinkage algorithm. By utilizing shorter window lengths to segment the data into more precise series, the method significantly improves the computational efficiency of spectral analysis while also enhancing its accuracy to a certain extent. Numerical models demonstrate that compared to the time–frequency spectra obtained using traditional methods such as wavelet transform, short-time Fourier transform, and generalized S-transform, the proposed method can achieve high-resolution extraction of the dominant frequencies of seismic waves, with superior noise resistance. Furthermore, its application in a research area in southern China shows that the method can effectively predict thicker sedimentary layers in low-frequency ranges and accurately identify thinner sedimentary layers in high-frequency ranges.https://www.mdpi.com/2076-3417/15/14/8034thickness predictionaquifer underground gas storagespectral analysisconstrained least squaresfast shrinkage algorithm |
| spellingShingle | Yanyan Ma Haixia Kang Weifeng Luo Yunxiao Zhang Lintao Luo A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints Applied Sciences thickness prediction aquifer underground gas storage spectral analysis constrained least squares fast shrinkage algorithm |
| title | A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints |
| title_full | A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints |
| title_fullStr | A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints |
| title_full_unstemmed | A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints |
| title_short | A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints |
| title_sort | high resolution spectral analysis method based on fast iterative least squares constraints |
| topic | thickness prediction aquifer underground gas storage spectral analysis constrained least squares fast shrinkage algorithm |
| url | https://www.mdpi.com/2076-3417/15/14/8034 |
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