Sparse constrained encoding multi-source full waveform inversion method based on K-SVD dictionary learning*
Guo Yun-dong 1,3, Huang Jian-Ping 1,2, Cui Chao 1,2, LI Zhen-Chun 1,2, LI Qing-Yang 1,3, and Wei Wei 4
1. School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China;
2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
3. Geophysical Exploration Research Institute of Zhongyuan Oilfi eld Company, Puyang 457001, China
4. SINOPEC Petroleum Exploration and Production Research Institute, Beijing 100083, China
Abstract Full waveform inversion (FWI) is an extremely important velocity-model-building method. However, it involves a large amount of calculation, which hindsers its practical application. The multi-source technology can reduce the number of forward modeling shots during the inversion process, thereby improving the efficiency. However, it introduces crossnoise problems. In this paper, we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning. The phase encoding technology is introduced to reduce crosstalk noise, whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results. The multiscale inversion method is adopted to further enhance the stability of FWI. Finally, the synthetic subsag model and the Marmousi model are set to test the eff ectiveness of the newly proposed method. Analysis of the results suggest the following: (1) The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability; (2) The proposed method can be combined with the time-domain multi-scale FWI strategy fl exibly to further avoid the local minimum and to improve the stability of inversion, which is of signifi cant importance for the inversion of the complex model.
This work was jointly supported by the National Science and Technology Major Project (Nos. 2016ZX05002-005-07HZ, 2016ZX05014-001-008HZ, and 2016ZX05026-002-002HZ), National Natural Science Foundation of China (Nos. 41720104006 and 41274124), Chinese Academy of Sciences Strategic Pilot Technology Special Project (A) (No. XDA14010303) , Shandong Province Innovation Project (No. 2017CXGC1602) and Independent Innovation (No.17CX05011).
About author: Guo Yundong is a PhD candidate in Geological Resources and Engineering at the China University of Petroleum. He was born in 1991 and received his undergraduate degree in Geophysical Prospecting and Engineering from China University of Petroleum in 2014. His research interests are full waveform inversion and seismic inversion imaging. Email: gyd_upc@163.com; 1476326813@qq.com
Correspondence author: Huang Jianping is a Doctoral Tutor of China University of Petroleum (East China). He received his undergraduate degree in geophysics from University of Science and Technology of China in 2003 and received his Doctor degree in geophysics from University of Science and Technology of China in 2008. His research interests are seismic forward modeling and inversion imaging. E-mail:jphuang@upc.edu.cn.
Cite this article:
. Sparse constrained encoding multi-source full waveform inversion method based on K-SVD dictionary learning*[J]. APPLIED GEOPHYSICS, 2020, 17(1): 111-123.
2020, Vol. 17 
