Time-lapse or 4D seismic survey is a crucial monitoring tool for CO₂ geological sequestration. Conventional time-lapse interpretation provides detailed characterization of CO₂ distribution in the storage unit. However, manual interpretation is labor-intensive and often inconsistent throughout the long monitoring history, due to the inevitable changes in seismic acquisition and processing technology and interpreter's subjectivity. We propose a neural network (NN)-based interpretation method that translates baseline and monitoring seismic images to the probability of CO₂ presence. We use a simplified 3D U-Net, whose training, validation and testing are all based on the Sleipner CO₂ storage project. The limited labels for training are derived from the interpreted CO₂ plume outlines within the internal sandstone layers for 2010. Then we apply the trained NN on different time-lapse seismic data sets from 1999 to 2010. The results suggest that our NN-based CO₂ interpretation has the following advantages: (a) high interpretation efficiency by automatic end-to-end mapping; (b) robustness against the processing-induced mismatch between the baseline and time-lapse inputs, relaxing the baseline reprocessing demands when compared to newly acquired or reprocessed time-lapse data sets; and (c) inherent interpretation consistency throughout multiple vintage data sets. Testing results with crafted time-lapse images unveil that the NN takes both amplitude difference and structural similarity into account for CO₂ interpretation. We also compare 2D and 3D U-Nets under the scenario of sparse 2D labels for training. The results suggest that the 3D U-Net provides more continuous interpretation at the cost of larger computational resources for training and application.

中文翻译：

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来自 4D Sleipner 地震图像的基于神经网络的 CO2 解释

延时或 4D 地震勘测是 CO ₂地质封存的重要监测工具。传统的延时解释提供了存储单元中 CO ₂分布的详细特征。然而，由于地震采集和处理技术以及解释者主观性的不可避免的变化，人工解释是劳动密集型的并且在整个漫长的监测历史中常常不一致。我们提出了一种基于神经网络 (NN) 的解释方法，可将基线和监测地震图像转换为 CO ₂存在的概率。我们使用简化的 3D U-Net，其训练、验证和测试均基于 Sleipner CO ₂存储项目。训练的有限标签来自2010 年内部砂岩层内解释的 CO ₂羽流轮廓。然后我们将训练的神经网络应用于 1999 年至 2010 年的不同时移地震数据集。结果表明我们基于神经网络的 CO ₂解释具有以下优点： (a) 自动端到端映射，解释效率高；(b) 对基线和延时输入之间处理引起的不匹配的鲁棒性，与新获取或重新处理的延时数据集相比，放宽了基线再处理的需求；(c) 贯穿多个年份数据集的内在解释一致性。使用精心制作的延时图像的测试结果表明，NN 在解释CO _{2 时}同时考虑了幅度差异和结构相似性。我们还在用于训练的稀疏 2D 标签场景下比较了 2D 和 3D U-Nets。结果表明，3D U-Net 以更大的训练和应用计算资源为代价提供了更连续的解释。