AVO Uncertainty Inversion Based on Multitask Variational Bayesian Neural Network

Solutions to the amplitude variation with offset (AVO) inverse problem are inherently nonunique. Thus, estimating the range of potential solutions is crucial. For this reason, uncertainty inversion is more appropriate than deterministic AVO inversion. However, most studies on deep learning (DL)-based inversion focus on deterministic prediction. In general, there are few studies on uncertainty inversion, mainly focused on poststack seismic data. In this study, we propose a DL-based AVO uncertainty inversion method based on an improved variational Bayesian neural network (VBNN) for predicting multiple elastic parameters. Moreover, to mitigate the issue of insufficient labeled data in inversion tasks, we combine the improved VBNN with a semi-supervised learning framework. Synthetic data experiments demonstrate that the proposed method exhibits higher accuracy and robustness to noisy seismic data than the well-known traditional Bayesian linearized inversion (BLI) method. The proposed method also has slightly higher inversion accuracy than the state-of-the-art improved-hybrid-seismic-prior-guided neural network (IHGNN). Moreover, the uncertainty estimation confirms that the proposed method can explore potential solutions to the AVO inverse problem more effectively and reasonably than the comparison methods. Furthermore, field data AVO inverse experiments verify that the proposed method can obtain reasonable predicted results and effectively reveal uncertainty in the inversion results.