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.


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