Deep learning based on self-supervised pre-training: Application on sandstone content prediction

Abstract

Deep learning has been widely used in various fields and showed promise in recent years. Therefore, deep learning is the future trend to realize seismic data’s intelligent and automatic interpretation. However, traditional deep learning only uses labeled data to train the model, and thus, does not utilize a large amount of unlabeled data. Self-supervised learning, widely used in Natural Language Processing (NLP) and computer vision, is an effective method of learning information from unlabeled data. Thus, a pretext task is designed with reference to Masked Autoencoders (MAE) to realize self-supervised pre-training of unlabeled seismic data. After pre-training, we fine-tune the model to the downstream task. Experiments show that the model can effectively extract information from unlabeled data through the pretext task, and the pre-trained model has better performance in downstream tasks.