3D Salt-HSM: Salt Segmentation Method Based on Hybrid Semi-Supervised and Multitask Learning

Salt bodies are significant reservoir structures, and there are still difficulties in interpreting them end-to-end from 3-D seismic data. Conventional semi-supervised learning struggles with obtaining high-quality pseudo labels early on, affecting subsequent model performance. Moreover, complex background noise hinders the accuracy of salt body predictions, while a strategy of gradually feeding training blocks leads to fragmented and confusing results. To address these challenges and restore realistic subsurface salt profiles, we have proposed an innovative, fully automated, and refined 3-D salt interpretation method called 3D Salt-HSM. In this method, we have designed a hybrid semi-supervised training paradigm based on stable pseudo labels and multilevel consistency constraints. This approach allows us to obtain high-quality pseudo labels for salt bodies and fully explore their features in unlabeled segmented blocks. We have also introduced a multitask learning strategy for fine interpretation of salt bodies, ranging from image level to pixel level. This strategy helps alleviate the adverse impact of interfering textures on salt body prediction. In addition, we have incorporated a contextual feature fusion module (CFFM) based on the multiscale context of salt bodies. This module enables the network to capture the global information of seismic images and achieve fine-grained salt body interpretation. In our experiments on the SEAM and F3 seismic datasets, we utilized only 3% of the labels for supervised learning, while the remaining data were used for unsupervised learning and validation. The experimental results demonstrate that 3D Salt-HSM outperforms previous state-of-the-art (SOTA) methods in terms of salt body segmentation performance, producing highly satisfactory results.