SaltISCG: Interactive Salt Segmentation Method Based on CNN and Graph Cut

Salt body extraction plays an important role in the analysis of salt structures and the exploration of oil and gas. Seismic attributes and edge detection algorithms, which require manual effort, are the conventional methods of extracting salt boundaries from seismic images. Convolutional neural networks (CNNs) have become the state-of-the-art automatic segmentation method for seismic interpretation. However, the fully automatic results of the extraction of salt boundaries may still need to be modified to become accurate and robust enough for practical production. We present a novel deep-learning-based interactive segmentation method for extracting salt boundaries. To incorporate the interaction points into our method, we transform positive and negative points into two Euclidean distance maps (EDMs), which are combined with seismic images to train our CNN model. The model is composed of a U-net and a pyramid pooling module (PPM), and it is trained on the Tomlinson Geophysical Services (TGS) Salt Identification Challenge dataset. Then, we use a graph cut algorithm to refine the likelihood maps predicted by our CNN model and, subsequently, update the salt boundaries. Some field examples show that the proposed method outperforms fully automatic CNN methods with a higher matching degree of the ground truth.

Automated summary

This study proposes a novel deep-learning-based interactive segmentation method for extracting salt boundaries. By transforming interaction points into Euclidean distance maps and training a CNN model with seismic images, this method achieves more accurate and robust salt boundary extraction. The results are further improved using a graph cut algorithm.