Automatic Fault Delineation in 3-D Seismic Images with Deep Learning: Data Augmentation or Ensemble Learning?

Delineating seismic faults is one of the main steps in seismic structure interpretation. Recently, deep learning (DL) models are used to automatic seismic fault interpretation. For the DL-based models, there are two widely used techniques, which can enhance the model performance, that is, data augmentation (DA) and ensemble learning (EL). Qualitatively and quantificationally analyzing the performances of these two techniques is a rarely studied domain. In this study, we make detailed comparisons between the DL models using DA and EL. For the DL model with DA, we first build a holistically nested Unet (HUnet) model by adopting the holistically nested module to the widely used Unet model. Then, we train a HUnet model by using the original and its augmented synthetic datasets (HUnet-D model for short). Besides, we train a Unet model in the same way as a comparison (Unet-D model for short). On the other hand, for the DL model with EL, we first obtain several individual HUnet models separately trained by only using a type of the augmented datasets for each time. Next, we propose a data-driven EL model to integrate these HUnet models. Specially, we propose an adjoint-net module for the EL model to extract the multi-scale features from seismic data, which benefits for checking and fine-tuning the fusing results. Finally, we qualitatively and quantificationally evaluate these DL models (Unet-D, HUnet-D, and EL-HUnet) using the synthetic validation dataset. Moreover, we apply these models to 3-D field data volumes for automatic fault interpretation. Compared with the coherence attribute, Unet-D and HUnet-D models, we find that the EL-HUnet model achieves the comparable model performance for effectively enhancing the precision and continuity of the detected faults.