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.
翻译:由数据驱动的断层检测被视为3D图像分割任务。 由合成数据培训的模型很难在某些调查中加以概括。 最近, 使用稀疏的人工 2D 切片培训 3D 断层分割被认为会产生令人乐观的结果, 但人工标签有许多虚假的负面标签( 异常说明), 不利于培训和检测性能。 有意在稀疏的 2D 标签下培训3D 断层网络, 压制假的负面标签, 我们分析培训过程梯度, 并提出Mask Dice (MD) 损失 。 此外, 断层是一个边缘特征, 而目前广泛用于发现断层的编码- 解密主流结构( 如 U- shape 网络 ) 也被认为不利于边缘代表。 因此, 提议了Fault- Net, 其设计是为了了解断层的特性, 使用高分辨率传播特征, 并嵌入多级的多级缩放组合块块以整合多级信息, 使得边端信息在传播和融合过程中得到充分保存, 从而能够通过少量的计算资源实现高级的高级性能, 。 。 实验性实验性实验显示, 更精确化和精确的精确的计算方法能支持在模拟的模型中, 使人类的精确性化的模型在进行更精确性化的精确性化的精确性化的计算, 。