Elastic Parameter Inversion Method of Pre-Stack Seismic Wave based on Deep Learning

Using seismic information for oil and gas exploration can improve the accuracy of exploration, which makes this technology become the research focus of oil and gas exploration. The inversion technology based on prestack seismic wave, which contains more elastic parameter information reflecting the characteristics of underground reservoirs, has become a popular technology in seismic inversion. In view of the shortcomings of traditional deep learning methods in the inversion of elastic parameters of pre-stack seismic waves, this paper proposes an improved combination method of convolutional neural network, gated recurrent unit and attention mechanism, which can comprehensively and deeply mine the data and obtain better prediction effect through the fusion of multiple models. The experimental results show that the proposed method can obtain the solution with higher inversion precision and has a good application prospect in the complex seismic wave impedance.