Large-Dimensional Seismic Inversion Based on Global Optimization and Autoencoder
Z. Gao, Z. Pan, J. Gao and Z. Xu
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: Deep Learning and Data Analytics – Seismic Applications A
Publication date: 03 June 2019
Info: Extended abstract, PDF ( 2.75Mb )
Price: € 20
Seismic inversion often involves nonlinear relationships between model and data and the misfit function usually has many local minima. Global optimization algorithms are well-known capable to search for the global minimum of a misfit function without requiring a good initial model. However, these algorithms can hardly work for large-dimensional cases because of the "curse of dimensionality" problem. In this paper, we mitigate this problem by introducing a neural network called autoencoder into seismic inversion and propose a new inversion method based on global optimization and autoencoder. Benefiting from the dimensionality reduction characteristics of autoencoder, in the proposed method, the original large-dimensional problem is transformed into a low-dimensional one that can be efficiently optimized by a global optimization algorithm. Preliminary numerical examples demonstrate that the proposed method can solve large-dimensional seismic inversion problem with a significant improvement in efficiency compared with conventional global optimization based method.