Few-shot learning for seismic facies segmentation via prototype learning

The mapping of seismic facies from seismic data is considered a multiclass image semantic segmentation problem. Despite the signification progress made by the deep learning methods in seismic prospecting, the dense prediction problem of seismic facies requires large amounts of annotated seismic facies data, which often are unavailable. These valuable labels are only helpful in one model and field due to geologic heterogeneity. To overcome these challenges, we have developed a few-shot seismic facies segmentation model. Few-shot learning has been designed to learn to perform with very few labels and we design reconstructing masked traces as a pretext task for self-supervised learning to obtain a good feature extractor. By these, this model can use all seismic data from different fields, which is different from image data as the texture-based data. With two different seismic data in turn as a meta-training set and a meta-testing set, our model works well in one- and five-shot settings, which means only one label and five labels, respectively.