A Deep Learning-Based Seismic Horizon Tracking Method With Uncertainty Encoding and Vertical Constraint

Deep learning-based seismic horizon tracking methods have been extensively researched in the past few years. However, the predicted results of previous methods are currently unstable and require additional processing to obtain reasonable interpretations, limiting their applications to field seismic data. There are two potential reasons for this: 1) the training labels may not consider the uncertainty in horizon interpretation and 2) the characteristics of the seismic data used for training may differ from those for prediction (domain shift). In addition, the previous deep learning methods are mostly based on point-by-point cost functions, which are not well-suitable for horizon tracking problems. To address these issues, we proposed a method that mimics the process of manual horizon interpretation to avoid the domain shift problem and regard horizon identification and tracking as a problem of conditional probability density estimation using deep learning. In the North Sea F3 seismic data experiments, our proposed method can predict horizons accurately and stably when we select only 1.6% of the seismic sections to create labels. Taking mean absolute error (MAE) and accuracy as evaluation indices, the MAE for single-horizon prediction is as low as 2.0 ms, and the accuracy reaches 98.1%. A surprising finding revealed by the experiments is that the predicted results could provide a perspective of underground structures, such as faults and salt domes, demonstrating the feasibility of our method in interpreting complex field seismic data.