Reconstruction of Sparsely Sampled Seismic Data via Residual U-Net
Reconstruction of Sparsely Sampled Seismic Data via Residual U-Net
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DOI：
10.1109/lgrs.2020.3035835
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发表时间：
2022
影响因子：
4.8
通讯作者：
Shuhang Tang;Yinshuai Ding;Hua-Wei Zhou;Heng Zhou
中科院分区：
文献类型：
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作者：
Shuhang Tang;Yinshuai Ding;Hua-Wei Zhou;Heng Zhou
当大量炮点和接收器丢失时，稀疏采样地震数据的重建对于保持地震图像的质量至关重要。我们提出了一种基于剩余U-Net机器学习架构的炮点-接收器-时间（SRT）域重建方法，用于稀疏2-D采集中采集的地震数据，并将其命名为SRT 2D-ResU-Net。SRT域保留了高水平的地震信号的连通性，这可能是主要的数据功能，重建算法relieon. We开发了一个“原位训练和预测”的工作流程，通过将采集区域分为两个不重叠的子区域：一个训练子区域，用于建立网络模型，使用定期采样的数据和测试子区域重建稀疏采样的数据，使用训练模型。为了建立一个参考基础，分析研究区的数据特征的变化，并量化重建的地震数据，我们设计了一个基线参考使用一小部分的现场数据。基线被适当地间隔开，并从训练和重建过程中排除。在海洋野外数据集上的实验结果表明，SRT 2D-ResU-Net在训练过程中能够有效地学习地震数据的特征，重建的缺失道与真实答案的平均相关度达到85%以上。
Reconstruction of sparsely sampled seismic data is critical for maintaining the quality of seismic images when significant numbers of shots and receivers are missing. We present a reconstruction method in the shot-receiver-time (SRT) domain based on a residual U-Net machine learning architecture, for seismic data acquired in a sparse 2-D acquisition and name it SRT2D-ResU-Net. The SRT domain retains a high level of seismic signal connectivity, which is likely the main data feature that the reconstructing algorithms rely on. We develop an “ in situ training and prediction” workflow by dividing the acquisition area into two nonoverlapping subareas: a training subarea for establishing the network model using regularly sampled data and a testing subarea for reconstructing the sparsely sampled data using the trained model. To establish a reference base for analyzing the changes in data features over the study area, and quantifying the reconstructed seismic data, we devise a baseline reference using a tiny portion of the field data. The baselines are properly spaced and excluded from the training and reconstruction processes. The results on a field marine data set show that the SRT2D-ResU-Net can effectively learn the features of seismic data in the training process, and the average correlation between the reconstructed missing traces and the true answers is over 85%.