Attention-Based 3-D Seismic Fault Segmentation Training by a Few 2-D Slice Labels

Detection faults in seismic data are a crucial step for seismic structural interpretation, reservoir characterization, and well placement. Some recent works regard it as an image segmentation task. The task of image segmentation requires huge labels, especially 3-D seismic data, which has a complex structure and lots of noise. Therefore, its annotation requires expert experience and a huge workload. In this study, we presented
$\lambda $
-binary cross-entropy (BCE) and
$\lambda $
-smooth
$L_{1}$
loss to effectively train 3D-CNN by some slices from 3-D seismic volume label, so that the model can learn the segmentation of 3-D seismic data from a few 2-D slices. In order to fully extract information from limited data and suppress seismic noise, we proposed an attention module that can be used for active supervision training and embedded in the network. The attention map label is generated by the original label and letting it supervise the attention module using the
$\lambda $
-smooth
$L_{1}$
loss. The experimental results demonstrate that the proposed loss function can extract 3-D seismic features from a few 2-D slice labels. And it also shows the advanced performance of the attention module, which can significantly suppress the noise in the seismic data while increasing the sensitivity of the model to the foreground. Finally, on the public test set, the proposed method achieved similar performance to using 3-D volume labels by using only 3.3% of the slices.