We propose to solve seismic interpretation problem by manually labelling very small (0.5%) fraction of inline and crossline sections of the seismic cube, followed by automatic segmentation of the rest of the cube by a neural network model.

There are several methods to improve the quality of segmentation. First, we use an additional input image, which is essentially an interpolation of orthogonal labelled images. Second, we describe two types of augmentations, which work particularly well for seismic segmentation, grid distortion and linear-harmonic transform. This workflow results in high quality segmentation and is a good candidate to be used in real world situations to reduce manual labelling.


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