| Title | Seismic multiple suppression based on a deep neural network method for marine data |
| Authors | Wang, Kunxi Hu, Tianyue Wang, Shangxu Wei, Jianxin |
| Affiliation | Peking Univ, Sch Earth & Space Sci, Beijing, Peoples R China China Univ Petr, State Key Lab Petr Resources & Prospecting, Beijing, Peoples R China |
| Keywords | INVERSE-SCATTERING SERIES POINT CFP APPROACH INTERNAL MULTIPLES ITERATIVE INVERSION SUBTRACTION REMOVAL ATTENUATION |
| Issue Date | Jul-2022 |
| Publisher | GEOPHYSICS |
| Abstract | Seismic multiples in marine seismic data can affect the identification of oil and gas reservoirs. The efficiency of traditional multiple suppression methods, such as the Radon transform, depends on the accuracy of the velocity model for primaries and multiples, and the assumption of random background noise. To attenuate multiples with background noise, a method of primary reconstruction using a deep neural network based on data augmentation training is proposed. The designed deep neural network (DNN) includes convolutional encoding and decoding processes. The convolutional encoding process uses convolutional layers and maximum pooling layers to learn the features of the primaries, multiples, and background noise in a seismic |
| URI | http://hdl.handle.net/20.500.11897/649514 |
| ISSN | 0016-8033 |
| DOI | 10.1190/geo2021-0206.1 |
| Indexed | EI SCI(E) |
| Appears in Collections: | 地球与空间科学学院 |
