Seismic acoustic-impedance (AI) inversion, which estimates the AI of the reservoir from seismic and other geophysical data, is a type of nonlinear inverse problem that faces the local minima issue during optimization. Without requiring an accurate initial model, global optimization methods have the ability to jump out of local minima and search for the optimal global solution. However, the low-efficiency nature of global optimization methods hinders their practical application, especially in large-scale AI inversion problems (AI inversion with a large number of traces). We have developed a new intelligent seismic AI inversion method based on global optimization and deep learning. In this method, global optimization is used to generate data sets for training a deep-learning network, and it is used to first accelerate and then surrogate global optimization. In other words, for large-scale seismic AI inversion, global optimization only inverts the AI model for a few traces, and the AI models of most traces are obtained by deep learning. The deep-learning architecture that we used to map from the seismic trace to its corresponding AI model is established based on U-Net. Because the time-consuming global optimization inversion procedure can be avoided for most traces, this method has a significant advantage over conventional global optimization methods in efficiency. To verify the effectiveness of our method, we compare its performance with the conventional global optimization method on 3D synthetic and field data examples. Compared to the conventional method, our method only needs approximately one-tenth of the computation time to build AI models with better accuracy.

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