  Detection faults in seismic data is 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 3D 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 present lambda-BCE and lambda-smooth L1loss to
effectively train 3D-CNN by some 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 data and suppress seismic noise, we
propose an attention module that can be used for active supervision training
and embedded in the network. The attention heatmap label is generated by the
original label, and letting it supervise the attention module using the
lambda-smooth L1loss. The experiment demonstrates the effectiveness of our loss
function, the method can extract 3D seismic features from a few 2D 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 model's sensitivity to the foreground. Finally, on the public test set, we
only use the 2D slice labels training that accounts for 3.3% of the 3D volume
label, and achieve similar performance to the 3D volume label training.
