Sequence-Constrained Multi-Task Horizon Tracking
Sequence-Constrained Multi-Task Horizon Tracking
复制标题
序列约束多任务水平跟踪
DOI：
10.1190/geo2022-0398.1
复制
发表时间：
2022-11
期刊：
影响因子：
3.3
通讯作者：
Lei Li
中科院分区：
文献类型：
--
作者：
Yilang Luo;Gulan Zhang;Jianjun Zhang;Yong Li;Yu Lin;Biao Li;Chenxi Liang;Lei Li
基于深度学习的自动地平线跟踪已经取得了可喜的成果，但仍然面临严重的跨地平线现象。因此，基于多任务学习、地震数据特征和地震层位序列（或位置）关系，提出了一种序列约束多任务层位跟踪（SMHT）方法，实现了高精度的层位自动跟踪。SMHT包含水平标签自动富集、多任务水平跟踪网络（MHTN）和水平序列约束损失函数。层位标签自动丰富的目的是自动生成目标层位标签的上下辅助层位标签，然后生成MHTN的辅助层位标签和目标层位标签对应的层位区域标签。MHTN包含共享层、辅助任务、主任务和层位序列约束层位校正。在MHTN中，共享层生成输入地震数据的多尺度特征图。辅助任务使用目标检测的概念来提取地平线区域（或概率），主任务在提取的地平线区域内提取高精度地平线。利用层位序列约束损失函数进行层位序列约束层位校正，以避免跨层现象，最终获得精确的层位跟踪结果。将MHTN应用于两个野外三维地震数据集，发现SMHT在自动层位跟踪方面表现良好。
Deep learning-based automatic horizon tracking has achieved promising results but still faces serious cross-horizon phenomena. Therefore, based on multitask learning, seismic data characteristics, and the seismic horizon sequence (or position) relationship, we have developed a sequence-constrained multitask horizon tracking (SMHT) method for high-precision automatic horizon tracking. SMHT contains the horizon label automatic enrichment, the multitask horizon tracking network (MHTN), and the horizon sequence-constrained loss function. Horizon label automatic enrichment aims to automatically generate the upper and lower auxiliary horizon labels of the target horizon label, and then the horizon region labels corresponding to the auxiliary and target horizon labels for MHTN. MHTN contains the shared layer, the auxiliary task, the main task, and the horizon sequence-constrained horizon correction. In MHTN, the shared layer generates multiscale feature maps of the input seismic data. The auxiliary task uses the concept of object detection to extract the horizon region (or probability), and the main task extracts the high-precision horizon within the extracted horizon region. The horizon sequence-constrained horizon correction with the horizon sequence-constrained loss function aims to avoid the cross-horizon phenomenon and finally obtain precise horizon tracking results. Application of MHTN to two field 3D seismic data sets finds that SMHT performs well in automatic horizon tracking.