Automated seismic semantic segmentation using attention U-Net

Seismic facies mapping from a 3D seismic cube is of significant value to various seismic interpretation and characterization tasks. Traditional facies mapping is based on examining sedimentary environments and stratigraphic sequences that provide distinct characteristics used for facies mapping. Given the complex nature of the task, manual facies mapping is typically time and labor consuming, and the quality of the decisions varies as a function of expertise. This complexity is further increased with the ever-increasing size of 3D seismic data sets. Deep-learning methods have indicated a promising potential to perform fast, accurate, and automated segmentation tasks. We investigate the application of machine-learning techniques, particularly state-of-the-art deep convolutional neural networks (CNNs), as a framework to perform accurate automated seismic facies pixel-wise segmentation. The workflow consists of a CNN-based U-Net architecture that adopts modern computer vision techniques. We develop three major changes to the standard U-Net to boost the performance for seismic semantic segmentation tasks: (1) using residual building blocks in the encoder, (2) using transformer-like attention gates after each residual block, and (3) using frequency spectrum data, in addition to seismic amplitude, as input to the network. We indicate that this implementation achieves higher accuracy metrics outperforming recently published state-of-the-art benchmarks. The performance of our method is validated using two 3D seismic data sets, the F3 Netherlands data set and the Penobscot data set acquired offshore Nova Scotia, Canada. Experimentation involves training on a set of samples and tuning the hyperparameters, followed by quantitative evaluation of the trained network. Our workflow produces high-quality segmentation with significantly reduced artifacts, improved edge detection, and improved lateral consistency throughout the seismic survey.