Semisupervised semantic segmentation for seismic interpretation

Abstract

Seismic interpretation plays an essential role in locating subsurface horizons and understanding geologic formations. Traditionally, constructing facies models for the entire reservoir often requires domain experts’ manual interpretations of many seismic images, which is time-consuming. Although labeled horizons are limited, we have many unlabeled seismic images. Therefore, we develop a semisupervised segmentation framework that uses unlabeled seismic data through a reconstruction loss to learn a robust encoder and improve horizon label predictions. To further mitigate the limited data problem, we incorporate data augmentation using the time-shifting method to mimic similar deformations in a reservoir. Finally, we investigate the prediction uncertainty using deep ensembles. Results indicate that unlabeled seismic data help us learn a better latent representation and achieve a higher prediction accuracy and a lower prediction uncertainty than solely using labeled data sets. We also compare semisupervised and supervised semantic segmentation further and understand when semisupervised learning performs better in terms of the number of labels, facies, and locations of predicted sections. We develop an active learning framework to label the most valuable unlabeled sections given the uncertainty estimation. We believe our work helps geophysicists reduce the amount of labeling efforts and achieve a higher facies classification accuracy with the same amount of labeling work.