Research on elastic parameter inversion method based on seismic facies-controlled deep learning network

Deep learning has been widely applied in the field of geophysics, and existing research literature has demonstrated that intelligent geophysical inversion methods have high vertical resolution but low horizontal resolution. The reason lies in the fact that existing horizontal constraint methods mainly adopt convolutional models, without fully considering other prior information of seismic data. Within the same sedimentary unit, seismic response characteristics vary gradually due to similar lithology and geological characteristics. Therefore, the seismic facies information extracted from seismic data is integrated into deep learning network to enhance the horizontal prediction stability of the network. Firstly, according to the spatial and temporal characteristics of seismic data, a fusion network of three-dimensional convolutional neural network (3D-CNN), gated recurrent unit (GRU) and attention mechanism is established to improve the vertical resolution of inversion results. Then, seismic facies classification of the target layer is achieved by applying the K-means clustering method. Subsequently, to improve the horizontal resolution of the inversion results, seismic facies classification is transformed into temporal encoding data using the position coding theory in natural language processing, to form a seismic facies-controlled deep learning network. Finally, the deep learning network is trained and tested in the thin interlayer model and practical application adopting a semi-supervised learning method. The results indicate that incorporating seismic facies-controlled technology in the deep learning network can improve the horizontal resolution of the inversion results.