Simultaneous Physics and Model-Guided Seismic Inversion Based on Deep Learning

Seismic inversion is one of the effective techniques to obtain elastic parameters for reservoir characterization. Deep learning is widely used in seismic inversion and has yielded many satisfactory results. The performance of the existing deep learning-based seismic inversion methods mainly depends on the network structure and a large number of effective training datasets. However, due to the limitation of expensive acquisition costs, it is difficult to obtain enough effective training datasets for network training in seismic surveys. To this end, we develop a double-dual network structure that incorporates both physics and model information to alleviate the dependence of deep learning methods on training data and even enables unsupervised learning and inversion. One of the dual networks is responsible for using the physical information to constrain the inversion results and ensure the physical validity of the predictions. The other dual network is responsible for using the priori information from the model domain to constrain the inversion results and improve the stability of the predictions. Ultimately, the two dual networks are coupled by a loss function to realize labeled/unlabeled network training and inversion applications. We then implement the method in a synthetic model as well as field data. The results are compared with traditional data-driven seismic inversion method and physics-guided data-driven seismic inversion method, and it is shown that the proposed method outperforms these two methods.