Stochastic inversion method based on compressed sensing frequency division waveform indication prior
The stochastic inversion method using logging data as conditional data and seismic data as constraint data has a higher vertical resolution than the conventional deterministic inversion method. However, it remains challenging to reduce the randomness of the prior obtained through conventional random...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2024.1505682/full |
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| author | Minmin Huang Leyi Xu Yanhui Zhu Ye He Zhiye Li Ying Lin |
| author_facet | Minmin Huang Leyi Xu Yanhui Zhu Ye He Zhiye Li Ying Lin |
| author_sort | Minmin Huang |
| collection | DOAJ |
| description | The stochastic inversion method using logging data as conditional data and seismic data as constraint data has a higher vertical resolution than the conventional deterministic inversion method. However, it remains challenging to reduce the randomness of the prior obtained through conventional random simulation techniques and to enhance its accuracy. To address this, we propose a stochastic inversion method based on compressed sensing frequency-division waveform indication prior. This method fully considers the geophysical mapping relationship between the observed seismic data and the parameters to be inverted across different frequency bands. And the correlation coefficients between the seismic data at the known points and the predicted points are obtained by solving the low-rank system of equations through the compressed sensing method. Consequently, pseudo-kriging simulation of well data is performed based on the similarity between known and predicted seismic waveforms, thus establishing prior information indicated by the seismic waveforms. On this basis, the stochastic inversion results are solved using a very fast simulated annealing method. Both model calculations and field data inversion effects demonstrate that the compressed sensing frequency-division waveform indication method effectively improves the accuracy of solving prior information under a low-rank matrix. Ultimately, the proposed stochastic inversion method based on compressed sensing frequency division waveform indication prior enhances the inversion accuracy and provides advantages in identifying underground oil and gas reservoirs. |
| format | Article |
| id | doaj-art-0830e7751b5b4d25b5d258412cb765eb |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-0830e7751b5b4d25b5d258412cb765eb2025-01-07T06:48:18ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-01-011210.3389/feart.2024.15056821505682Stochastic inversion method based on compressed sensing frequency division waveform indication priorMinmin HuangLeyi XuYanhui ZhuYe HeZhiye LiYing LinThe stochastic inversion method using logging data as conditional data and seismic data as constraint data has a higher vertical resolution than the conventional deterministic inversion method. However, it remains challenging to reduce the randomness of the prior obtained through conventional random simulation techniques and to enhance its accuracy. To address this, we propose a stochastic inversion method based on compressed sensing frequency-division waveform indication prior. This method fully considers the geophysical mapping relationship between the observed seismic data and the parameters to be inverted across different frequency bands. And the correlation coefficients between the seismic data at the known points and the predicted points are obtained by solving the low-rank system of equations through the compressed sensing method. Consequently, pseudo-kriging simulation of well data is performed based on the similarity between known and predicted seismic waveforms, thus establishing prior information indicated by the seismic waveforms. On this basis, the stochastic inversion results are solved using a very fast simulated annealing method. Both model calculations and field data inversion effects demonstrate that the compressed sensing frequency-division waveform indication method effectively improves the accuracy of solving prior information under a low-rank matrix. Ultimately, the proposed stochastic inversion method based on compressed sensing frequency division waveform indication prior enhances the inversion accuracy and provides advantages in identifying underground oil and gas reservoirs.https://www.frontiersin.org/articles/10.3389/feart.2024.1505682/fullcompressed sensingfrequency division waveform indicationprior informationseismic stochastic inversionelastic impedance (EI) |
| spellingShingle | Minmin Huang Leyi Xu Yanhui Zhu Ye He Zhiye Li Ying Lin Stochastic inversion method based on compressed sensing frequency division waveform indication prior Frontiers in Earth Science compressed sensing frequency division waveform indication prior information seismic stochastic inversion elastic impedance (EI) |
| title | Stochastic inversion method based on compressed sensing frequency division waveform indication prior |
| title_full | Stochastic inversion method based on compressed sensing frequency division waveform indication prior |
| title_fullStr | Stochastic inversion method based on compressed sensing frequency division waveform indication prior |
| title_full_unstemmed | Stochastic inversion method based on compressed sensing frequency division waveform indication prior |
| title_short | Stochastic inversion method based on compressed sensing frequency division waveform indication prior |
| title_sort | stochastic inversion method based on compressed sensing frequency division waveform indication prior |
| topic | compressed sensing frequency division waveform indication prior information seismic stochastic inversion elastic impedance (EI) |
| url | https://www.frontiersin.org/articles/10.3389/feart.2024.1505682/full |
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