Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections
Reverse-time migration (RTM) has the advantage that it can handle steep dipping structures and offer high-resolution images of the complex subsurface. Nevertheless, there are some limitations to the chosen initial model, aperture illumination and computation efficiency. RTM has a strong dependency o...
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| Format: | Article |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/4012 |
| _version_ | 1797603514565787648 |
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| author | Shang Huang Daniel Trad |
| author_facet | Shang Huang Daniel Trad |
| author_sort | Shang Huang |
| collection | DOAJ |
| description | Reverse-time migration (RTM) has the advantage that it can handle steep dipping structures and offer high-resolution images of the complex subsurface. Nevertheless, there are some limitations to the chosen initial model, aperture illumination and computation efficiency. RTM has a strong dependency on the initial velocity model. The RTM result image will perform poorly if the input background velocity model is inaccurate. One solution is to apply least-squares reverse-time migration (LSRTM), which updates the reflectivity and suppresses artifacts through iterations. However, the output resolution still depends heavily on the input and accuracy of the velocity model, even more than for standard RTM. For the aperture limitation, RTM with multiple reflections (RTMM) is instrumental in improving the illumination but will generate crosstalks because of the interference between different orders of multiples. We proposed a method based on a convolutional neural network (CNN) that behaves like a filter applying the inverse of the Hessian. This approach can learn patterns representing the relation between the reflectivity obtained through RTMM and the true reflectivity obtained from velocity models through a residual U-Net with an identity mapping. Once trained, this neural network can be used to enhance the quality of RTMM images. Numerical experiments show that RTMM-CNN can recover major structures and thin layers with higher resolution and improved accuracy compared with the RTM-CNN method. Additionally, the proposed method demonstrates a significant degree of generalizability across diverse geology models, encompassing complex thin layers, salt bodies, folds, and faults. Moreover, The computational efficiency of the method is demonstrated by its lower computational cost compared with LSRTM. |
| first_indexed | 2024-03-11T04:33:13Z |
| format | Article |
| id | doaj.art-9f7f85d96481434ab1a946af95c33ed6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:33:13Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9f7f85d96481434ab1a946af95c33ed62023-11-17T21:17:51ZengMDPI AGSensors1424-82202023-04-01238401210.3390/s23084012Convolutional Neural-Network-Based Reverse-Time Migration with Multiple ReflectionsShang Huang0Daniel Trad1Department of Geoscience, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, CanadaDepartment of Geoscience, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, CanadaReverse-time migration (RTM) has the advantage that it can handle steep dipping structures and offer high-resolution images of the complex subsurface. Nevertheless, there are some limitations to the chosen initial model, aperture illumination and computation efficiency. RTM has a strong dependency on the initial velocity model. The RTM result image will perform poorly if the input background velocity model is inaccurate. One solution is to apply least-squares reverse-time migration (LSRTM), which updates the reflectivity and suppresses artifacts through iterations. However, the output resolution still depends heavily on the input and accuracy of the velocity model, even more than for standard RTM. For the aperture limitation, RTM with multiple reflections (RTMM) is instrumental in improving the illumination but will generate crosstalks because of the interference between different orders of multiples. We proposed a method based on a convolutional neural network (CNN) that behaves like a filter applying the inverse of the Hessian. This approach can learn patterns representing the relation between the reflectivity obtained through RTMM and the true reflectivity obtained from velocity models through a residual U-Net with an identity mapping. Once trained, this neural network can be used to enhance the quality of RTMM images. Numerical experiments show that RTMM-CNN can recover major structures and thin layers with higher resolution and improved accuracy compared with the RTM-CNN method. Additionally, the proposed method demonstrates a significant degree of generalizability across diverse geology models, encompassing complex thin layers, salt bodies, folds, and faults. Moreover, The computational efficiency of the method is demonstrated by its lower computational cost compared with LSRTM.https://www.mdpi.com/1424-8220/23/8/4012convolutional neural networkreverse-time migrationsurface-related multiples |
| spellingShingle | Shang Huang Daniel Trad Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections Sensors convolutional neural network reverse-time migration surface-related multiples |
| title | Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections |
| title_full | Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections |
| title_fullStr | Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections |
| title_full_unstemmed | Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections |
| title_short | Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections |
| title_sort | convolutional neural network based reverse time migration with multiple reflections |
| topic | convolutional neural network reverse-time migration surface-related multiples |
| url | https://www.mdpi.com/1424-8220/23/8/4012 |
| work_keys_str_mv | AT shanghuang convolutionalneuralnetworkbasedreversetimemigrationwithmultiplereflections AT danieltrad convolutionalneuralnetworkbasedreversetimemigrationwithmultiplereflections |