Seismic Horizon Identification Using Semi-Supervised Learning With Virtual Adversarial Training

Seismic horizon extraction is important for subsurface structure interpretation and reservoir modeling. Recently developed learning-based seismic interpretation methods show great success in the case of sufficient labeled data but may fail when the labels are limited. Thus, we propose a simple but effective network for seismic horizon identification using only a limited number of labels. To avoid overfitting in the training, we introduce the mechanism of semi-supervised learning (SSL) with virtual adversarial training (VAT). With several seed points, the method can provide a good prediction and suggest regions lacking control points. By adding several seed points in these suggested regions, the performance of the network can be further improved, which can be regarded as an interactive way. In addition, iteratively retraining the network by using the previous high-confidence prediction can further refine the horizon identification. We, finally, compute a full horizon surface without holes and outliers by optimally fitting the horizon points identified by our SSL and reflection slopes estimated from the seismic amplitude image. Applications to two field datasets show our method is superior to conventional methods in picking a seismic horizon with significant waveform variations or across complex discontinuities, such as faults.