Sparse constrained encoding multi-source full waveform inversion method based on K-SVD dictionary learning
- PDF / 3,639,074 Bytes
- 13 Pages / 595.22 x 842 pts (A4) Page_size
- 31 Downloads / 186 Views
Sparse constrained encoding multi-source full waveform inversion method based on K-SVD dictionary learning* Guo Yun-dong1,3, Huang Jian-Ping1,2, Cui Chao1,2, LI Zhen-Chun1,2, LI Qing-Yang1,3, and Wei Wei4 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 effectiveness 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 flexibly to further avoid the local minimum and to improve the stability of inversion, which is of significant importance for the inversion of the complex model. Keywords: K-SVD dictionary, sparsity constraint, polarity encoding, multi-source, full waveform inversion
Introduction Full waveform inversion (FWI) in time domain was proposed by Tarantola (1984) based on the generalized
least squares method. Since then, it has been an important velocity modeling method. Theoretically, FWI can make full use of the amplitude, phase, travel time, waveform, and other information contained in seismic data (Tarantola,1984; Pratt et al., 1998; Virieux
Manuscript received by the Editor June 12, 2018; revised manuscript received November 11, 2019. *This work was jointly supported by the National Science and Technology Major Project (Nos. 2016ZX05002-00507HZ, 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). 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 Oilfield Company, Puyang 457001, China 4. SINOPEC Petroleum Exploration and Production Research Institute, Beijing 100083, China ♦Corresponding author: Huang Jian-Ping (Email: [email protected]) ©2020 The Editorial Departmen
Data Loading...
