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.
翻译:地震数据中的检测缺陷是地震结构解释、 储油层特征描述和井位设置的关键步骤。 一些最近的工作将它视为一个图像分割任务。 图像分割的任务需要巨大的标签, 特别是3D地震数据, 其结构复杂, 噪音很大。 因此, 它的注解需要专家经验和巨大的工作量。 在这个研究中, 我们用3D 地震数据的某些切片来展示3D- CNN 有效培训3D- CNN 。 这样模型就可以从几个 2D 切片中学习3D 地震数据的分解。 为了从有限的数据中充分提取信息并抑制地震噪音, 我们建议了一个关注模块, 特别是 3D 地震数据, 这个模块可用于积极的监督培训, 并嵌入网络。 注意热映标签是由原始标签生成的, 让它用 lambda- smooth L1loss 来监督关注模块。 实验表明我们的损失功能的有效性, 方法可以从几个 2D 切片标签中提取 3D 地震特征。 它还显示关注模块的高级性表现, 能够大大抑制地震标定为3D 3D 标准, 最后, 我们只能在地震标定的3D 的标签上, 3D 的立标定的立为3D 度上, 的立标定的立标为3D 。