Seismic inversion via closed-loop fully convolutional residual network and transfer learning

Abstract

Because deep-learning networks can “learn” the complex mapping function between labeled inputs and outputs, they have shown great potential in seismic inversion. Conventional deep-learning algorithms require a large amount of labeled data for sufficient training. However, in practice, the number of well logs is limited. To address this problem, we have adopted a closed-loop fully convolutional residual network (FCRN) combined with a transfer learning strategy for seismic inversion. This closed-loop FCRN consists of an inverse network and a forward network. The inverse network predicts the inversion target from seismic data, whereas the forward network calculates seismic data from the inversion target. The inverse network is initialized by pretraining on the Marmousi2 model and is fine-tuned with the limited labeled data around the wells through transfer learning, to suit the target seismic data. The forward network is initialized by training with the limited labeled data around the wells. In this way, the closed-loop network is well initialized to ensure relatively good convergence. Then, the misfit of the limited labeled data and the error between the true and the forward seismic data are used to regularize the training of the initialized closed-loop network. The inverse network of the optimized closed-loop network is used to obtain the final inversion results. Our workflow can be used for velocity, density, and impedance inversion from poststack seismic data. We take velocity inversion as an example to illustrate the effectiveness of the method. The experimental results show that closed-loop FCRN with transfer learning is superior to open-loop FCRN with better lateral continuity and velocity details. Closed-loop FCRN can effectively predict velocity with high accuracy on synthetic data, has good antinoise performance, and can also be effectively used for field data with spatial heterogeneity.