Seismic Fault Segmentation via 3D-CNN Training by a Few 2D Slices Labels
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Detection faults in seismic data is a crucial step for seismic structural interpretation, reservoir characterization and well placement, and it is full of challenges. Some recent works regard fault detection as an image segmentation task. The task of image segmentation requires a large amount of data labels, especially 3D seismic data, which has a complex structure and a lot of noise. Therefore, its annotation requires expert experience and a huge workload, wrong labeling and missing labeling will affect the segmentation performance of the model. In this study, we present a new binary cross-entropy and smooth L1 loss (λ-BCE and λ-smooth L1) to effectively train 3D-CNN by sampling some 2D slices from 3D seismic data, so that the model can learn the segmentation of 3D seismic data from a few 2D slices. In order to fully extract information from limited and low-dimensional data and suppress seismic noise, we propose an attention module that can be used for active supervision training (Active Attention Module, AAM) and embedded in the network to participate in the differentiation and optimization of the model. During training, the attention heatmap target is generated by the original binary label, and letting it supervise the attention module using the λ-smooth L1 loss. Qualitative experiments show that our method can extract 3D seismic features from a few 2D slices labels on real data, to segment a complete fault volume. Through visualization, the segmentation effect achieves state-of-the-art. Quantitative experiments on synthetic data prove the effectiveness of our training method and attention module. Experiments show that using our method, labeling one 2D slice every 30 frames at least (3.3 the original label), the model can achieve a segmentation performance similar to that of a 3D label.
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