Seismic horizon detection with neural networks
This work addresses the need for more reliable and reproducible methods in geophysics for automating seismic horizon tracking, though it appears incremental compared to existing CNN-based approaches.
The paper tackles the problem of seismic horizon detection by applying a binary segmentation approach with neural networks to multiple real seismic cubes, focusing on inter-cube generalization, but does not provide specific numerical results.
Over the last few years, Convolutional Neural Networks (CNNs) were successfully adopted in numerous domains to solve various image-related tasks, ranging from simple classification to fine borders annotation. Tracking seismic horizons is no different, and there are a lot of papers proposing the usage of such models to avoid time-consuming hand-picking. Unfortunately, most of them are (i) either trained on synthetic data, which can't fully represent the complexity of subterranean structures, (ii) trained and tested on the same cube, or (iii) lack reproducibility and precise descriptions of the model-building process. With all that in mind, the main contribution of this paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model.