  Data-driven fault detection has been regarded as a 3D image segmentation
task. The models trained from synthetic data are difficult to generalize in
some surveys. Recently, training 3D fault segmentation using sparse manual 2D
slices is thought to yield promising results, but manual labeling has many
false negative labels (abnormal annotations), which is detrimental to training
and consequently to detection performance. Motivated to train 3D fault
segmentation networks under sparse 2D labels while suppressing false negative
labels, we analyze the training process gradient and propose the Mask Dice (MD)
loss. Moreover, the fault is an edge feature, and current encoder-decoder
architectures widely used for fault detection (e.g., U-shape network) are not
conducive to edge representation. Consequently, Fault-Net is proposed, which is
designed for the characteristics of faults, employs high-resolution propagation
features, and embeds MultiScale Compression Fusion block to fuse multi-scale
information, which allows the edge information to be fully preserved during
propagation and fusion, thus enabling advanced performance via few
computational resources. Experimental demonstrates that MD loss supports the
inclusion of human experience in training and suppresses false negative labels
therein, enabling baseline models to improve performance and generalize to more
surveys. Fault-Net is capable to provide a more stable and reliable
interpretation of faults, it uses extremely low computational resources and
inference is significantly faster than other models. Our method indicates
optimal performance in comparison with several mainstream methods.
