3-D Seismic Fault Detection Using Fault Orthogonal Annotation and Barely Supervised Learning

Among deep learning-based seismic fault detection approaches, most of the training datasets are generated by using synthetic methods to enhance the diversity of training data. However, due to the feature differences between synthetic seismic data and real data, and the difficulty of synthesizing all types of actual geological structures, networks trained using synthetic data exhibit poor generalization performance in practical data applications. In this study, we propose a barely-supervised-learning-based seismic fault detection scheme. The training data are extracted from the real seismic data and the fault labels are manually annotated by the interpreter. In this scheme, only 10% of the training data need to be labeled. Furthermore, to avoid dense annotation of faults on 3-D seismic data, we adopt an orthogonal annotation strategy, where only one inline section and one horizontal slice are annotated for one $128 {\times}128 \times 128$ training sample. As a result, the pixels that have been actually annotated constitute merely 0.1563% of the total voxels in the training sample. Then, the orthogonal annotation is extended to the dense annotation of training data by using a fault registration method. We confirmed the feasibility and effectiveness of this approach using synthetic data. Accurate fault detection can be achieved by annotating a few orthogonal sections and slices from actual samples. Further application on two real seismic data demonstrated that the fault detection by this method is more continuous and accurate compared to the results predicted by other methods.