Nonstationary inversion-based directional deconvolution of airgun array signature*
Li Hao, Li Guo-Fa, Guo Xiang-Hui, Sun Xi-Ping, and Wang Jian-Fu
1. CNPC Key Laboratory of Geophysical Prospecting, China University of Petroleum, Beijing 102249, China.
2. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China.
3. Research Institute of Petroleum Exploration and Development, Petrochina, Beijing 100083, China.
4. Dagang Oil Field, Petrochina, Tianjin 300280, China.
Abstract Airgun arrays are widely used in marine seismic exploration because signatures excited by airgun arrays have high energy and high-peak bubble ratio, whereas the considerable length and width of the array and ghost reflections make the airgun array signature directional. As a result, the relation of the reflection amplitude with the incident and azimuth angles is variable. This means that the directivity of the airgun array results in a nonstationary wavelet and distorts the relation of the amplitude variation with the incident and azimuth angles. To remove the directivity effect, we propose a nonstationary inversion-based directional deconvolution. At first, the signature as a function of take-off angle and azimuth angle is calculated using the spatial configuration of the airgun array and the near-field signatures. Then, based on the velocity model, the time-variant take-off angles are estimated and directional filters are designed using the take-off angles. Finally, the directivity-dependent signatures are shaped to the signature right below the airgun array using nonstationary inversion in the directional deconvolution.
*This research is financially supported by the National Natural Science Foundation of China (No. 41474109) and the China National Petroleum Corporation under grant number 2016A-33.
Corresponding Authors: Li Guo-Fa (Email: ligf@cup.edu.cn)
E-mail: ligf@cup.edu.cn
About author: Li Hao received his M.S. in Geological Engineering (Geophysical Exploration) from China University of Petroleum, Beijing, in 2012. He is presently a Ph.D. student at China University of Petroleum, Beijing, in Geological Resources and Engineering. His research interests are high-resolution seismic data processing and complex reservoir prediction.
2019, Vol. 16 
