Incremental semi-supervised learning for intelligent seismic facies identification

Intelligent seismic facies identification based on deep learning can alleviate the time-consuming and labor-intensive problem of manual interpretation, which has been widely applied. Supervised learning can realize facies identification with high efficiency and accuracy; however, it depends on the usage of a large amount of well-labeled data. To solve this issue, we propose herein an incremental semi-supervised method for intelligent facies identification. Our method considers the continuity of the lateral variation of strata and uses cosine similarity to quantify the similarity of the seismic data feature domain. The maximum-difference sample in the neighborhood of the currently used training data is then found to reasonably expand the training sets. This process continuously increases the amount of training data and learns its distribution. We integrate old knowledge while absorbing new ones to realize incremental semi-supervised learning and achieve the purpose of evolving the network models. In this work, accuracy and confusion matrix are employed to jointly control the predicted results of the model from both overall and partial aspects. The obtained values are then applied to a three-dimensional (3D) real dataset and used to quantitatively evaluate the results. Using unlabeled data, our proposed method acquires more accurate and stable testing results compared to conventional supervised learning algorithms that only use well-labeled data. A considerable improvement for small-sample categories is also observed. Using less than 1% of the training data, the proposed method can achieve an average accuracy of over 95% on the 3D dataset. In contrast, the conventional supervised learning algorithm achieved only approximately 85%.