TY - JOUR
T1 - High resolution inversion of seismic wavelet and reflectivity using iterative deep neural networks
AU - Chen, Daoyu
AU - Gao, Jinghuai
AU - Hou, Yupeng
AU - Gao, Zhaoqi
N1 - Publisher Copyright:
© 2019 SEG
PY - 2019/8/10
Y1 - 2019/8/10
N2 - In this work, we propose a deep learning based data-driven method for high resolution inversion of seismic data. The method splits the inversion into two subproblems: one inverts the seismic wavelet and the other for reflectivity. Using a partially learned approach, the proposed method simultaneously estimates the wavelet and reflectivity in an alternative way, and realized by deep neural networks (DNN). Both synthetic and field data examples clearly demonstrate the advantages of the proposed method in reducing the prediction error, ensuring the sparsity of the reflectivity and improving the lateral stability.
AB - In this work, we propose a deep learning based data-driven method for high resolution inversion of seismic data. The method splits the inversion into two subproblems: one inverts the seismic wavelet and the other for reflectivity. Using a partially learned approach, the proposed method simultaneously estimates the wavelet and reflectivity in an alternative way, and realized by deep neural networks (DNN). Both synthetic and field data examples clearly demonstrate the advantages of the proposed method in reducing the prediction error, ensuring the sparsity of the reflectivity and improving the lateral stability.
UR - https://www.scopus.com/pages/publications/85121869000
U2 - 10.1190/segam2019-3216178.1
DO - 10.1190/segam2019-3216178.1
M3 - 会议文章
AN - SCOPUS:85121869000
SN - 1052-3812
SP - 2538
EP - 2542
JO - SEG Technical Program Expanded Abstracts
JF - SEG Technical Program Expanded Abstracts
T2 - Society of Exploration Geophysicists International Exposition and 89th Annual Meeting, SEG 2019
Y2 - 15 September 2019 through 20 September 2019
ER -