Seismic Facies Classification Using Label-Integrated and VMD-Augmented Transformer
Jinlong Huo, Naihao Liu, Zhaohui Xu, Xinguang Wang, Jinghuai Gao
Abstract
Seismic facies classification is crucial in interpreting subsurface geological structures for oil and gas exploration. Traditional methods for seismic facies classification usually rely on handcrafted features and heuristic rules, limiting their ability to capture complex geological patterns. We suggest a label-integrated and VMD-augmented transformer (LIVAT) to address these issues, which refers to transformers’ embedding stage infused with seismic facies labels and uses variational mode decomposition (VMD) to augment training data. Four advanced time-series transformer models are selected to verify the effectiveness of our proposed label-integrated embedding and VMD-augmentation on F3 Netherlands and New Zealand Parihaka datasets. Moreover, we evaluate the performance of LIVAT by comparing it with convolutional neural networks (CNNs) and bidirectional long short-term networks (BiLSTM) in few-shot learning. Experimental results demonstrate that LIVAT achieves superior classification accuracy and outperforms existing deep learning methods, showcasing its potential as a powerful tool for automatic seismic facies interpretation.