Traditional manual interpretation of seismic facies relies heavily on the experience of interpreters,resulting in low effi-ciency and strong subjectivity.In recent years,deep learning techniques,represented by deep neural networks based on unsupervised learning and supervised learning,have played an important role in the intelligent identification of seismic facies.However,the prob-lem of low accuracy of identification has vexed unsupervised learning with no guidance of prior knowledge in practical application,while supervised learning relies on a large amount of labelled information,which is often unavailable in practice.We propose a method of contrastive semi-supervised learning to optimize the model for seismic facies identification,which uses unlabelled and la-belled data to learn common characteristics of similar samples and the discrepancies among dissimilar samples so as to minimize intra-class distance for similar facies and maximize inter-class distance for different facies as much as possible.The facies and learn-ed features are correlated using a small number of labels to achieve high-precision identification of seismic facies in the region of in-terest.The proposed method was successfully applied to the public SEAM Al dataset and a filed dataset from the South China Sea.Compared with conventional supervised methods for seismic facies recognition,the proposed method can identify different types of seismic facies more accurately with a small number of labels.
维普
万方数据
