Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering
The velocity of seismic data can initially be established by identifying energy clusters on velocity spectra at different moments, which is crucial to the migration imaging and the stacking of common midpoint (CMP) gathers in the seismic data processing. However, the identification of energy cluster...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1039683/full |
| _version_ | 1797951367304708096 |
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| author | Li-De Wang Jie Wu Xing-Rong Xu Hua-Hui Zeng Yang Gao Wen-Qing Liu |
| author_facet | Li-De Wang Jie Wu Xing-Rong Xu Hua-Hui Zeng Yang Gao Wen-Qing Liu |
| author_sort | Li-De Wang |
| collection | DOAJ |
| description | The velocity of seismic data can initially be established by identifying energy clusters on velocity spectra at different moments, which is crucial to the migration imaging and the stacking of common midpoint (CMP) gathers in the seismic data processing. However, the identification of energy clusters currently relies on manual work, with low efficiency and different standards. With the increasing application of wide-frequency, wide-azimuth, and high-density seismic exploration technology, the amount of seismic data has increased significantly, greatly increasing the cost of manual labor and time. In this paper, an intelligent velocity picking method based on the Chan–Vese (CV) model and mean-shift clustering algorithm was proposed. It can be divided into three steps. First, a velocity trend band is set up on the velocity spectrum by experts to avoid multiples and other noises. Then, the velocity trend band is applied to the Chan–Vese model as the initial time condition to segment the velocity spectrum and obtain the velocity candidate region. Finally, mean-shift clustering is adopted to cluster the useful energy clusters retained in the candidate region derived from the Chan–Vese model. When implementing the mean-shift clustering algorithm, the Gaussian kernel function and the energy of the velocity spectrum are utilized to control the efficiency and accuracy of the cluster. The tests of the model and real data prove that the proposed method can dramatically improve the accuracy and efficiency of velocity picking compared with the K-means and manual picking method. |
| first_indexed | 2024-04-10T22:30:30Z |
| format | Article |
| id | doaj.art-32ba2a6dec474088b04c359cc6ec4f80 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T22:30:30Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-32ba2a6dec474088b04c359cc6ec4f802023-01-17T05:42:13ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011110.3389/feart.2023.10396831039683Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clusteringLi-De Wang0Jie Wu1Xing-Rong Xu2Hua-Hui Zeng3Yang Gao4Wen-Qing Liu5Research Institute of Petroleum Exploration & Development-Northwest (NWGI), PetroChina, Lanzhou, ChinaResearch Institute of Petroleum Exploration & Development-Northwest (NWGI), PetroChina, Lanzhou, ChinaResearch Institute of Petroleum Exploration & Development-Northwest (NWGI), PetroChina, Lanzhou, ChinaResearch Institute of Petroleum Exploration & Development-Northwest (NWGI), PetroChina, Lanzhou, ChinaChina University of Petroleum, Beijing, ChinaResearch Institute of Petroleum Exploration & Development-Northwest (NWGI), PetroChina, Lanzhou, ChinaThe velocity of seismic data can initially be established by identifying energy clusters on velocity spectra at different moments, which is crucial to the migration imaging and the stacking of common midpoint (CMP) gathers in the seismic data processing. However, the identification of energy clusters currently relies on manual work, with low efficiency and different standards. With the increasing application of wide-frequency, wide-azimuth, and high-density seismic exploration technology, the amount of seismic data has increased significantly, greatly increasing the cost of manual labor and time. In this paper, an intelligent velocity picking method based on the Chan–Vese (CV) model and mean-shift clustering algorithm was proposed. It can be divided into three steps. First, a velocity trend band is set up on the velocity spectrum by experts to avoid multiples and other noises. Then, the velocity trend band is applied to the Chan–Vese model as the initial time condition to segment the velocity spectrum and obtain the velocity candidate region. Finally, mean-shift clustering is adopted to cluster the useful energy clusters retained in the candidate region derived from the Chan–Vese model. When implementing the mean-shift clustering algorithm, the Gaussian kernel function and the energy of the velocity spectrum are utilized to control the efficiency and accuracy of the cluster. The tests of the model and real data prove that the proposed method can dramatically improve the accuracy and efficiency of velocity picking compared with the K-means and manual picking method.https://www.frontiersin.org/articles/10.3389/feart.2023.1039683/fullvelocity spectrumintelligent velocity pickingChan–Vesemean-shift clusteringexpert experience constraint |
| spellingShingle | Li-De Wang Jie Wu Xing-Rong Xu Hua-Hui Zeng Yang Gao Wen-Qing Liu Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering Frontiers in Earth Science velocity spectrum intelligent velocity picking Chan–Vese mean-shift clustering expert experience constraint |
| title | Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering |
| title_full | Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering |
| title_fullStr | Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering |
| title_full_unstemmed | Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering |
| title_short | Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering |
| title_sort | intelligent velocity picking considering an expert experience based on the chan vese model and mean shift clustering |
| topic | velocity spectrum intelligent velocity picking Chan–Vese mean-shift clustering expert experience constraint |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1039683/full |
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