标题： 用于地震图像超分辨率的Transformer 显示英文标题

标题： Transformer for seismic image super-resolution

摘要： 地震叠加或偏移得到的地震图像通常具有低信噪比(SNR)、低主频和在深度(或时间)及偏移距维度上稀疏采样的特征。 为了提高地震图像的分辨率，我们提出了一种基于深度学习的方法，在一步中实现超分辨率(SR)，即同时进行去噪、插值和频率外推。 我们设计了一个地震图像超分辨率Transformer(SIST)，以提取和融合局部和全局特征，更关注有效事件(层、褶皱和断层等)的能量和延伸形状。 从噪声地震图像中通过Canny算法提取输入图像的边缘图像作为掩码，生成双通道输入数据，这提高了振幅保持并减少了噪声的干扰。 包含Swin-Transformer块和残差连接的残差组构成了SIST的骨干，它们在预设大小的窗口中提取全局特征，同时减少计算成本。 像素洗牌层用于对骨干输出的特征图进行上采样以改善边缘，同时通过跳跃连接对输入数据进行上采样以增强最终图像的振幅保持，特别是对于清晰弱事件。 创建了具有复杂地质结构的三维合成地震体积，其中一半体积的振幅是强弱混合的，然后随机选择二维切片生成训练数据集，以很好地适应现场数据进行监督学习。 在不同勘探区域的合成和现场数据上的数值测试证明了我们方法的可行性。 显示英文摘要

摘要： Seismic images obtained by stacking or migration are usually characterized as low signal-to-noise ratio (SNR), low dominant frequency and sparse sampling both in depth (or time) and offset dimensions. For improving the resolution of seismic images, we proposed a deep learning-based method to achieve super-resolution (SR) in only one step, which means performing the denoising, interpolation and frequency extrapolation at the same time. We design a seismic image super-resolution Transformer (SIST) to extract and fuse local and global features, which focuses more on the energy and extension shapes of effective events (horizons, folds and faults, etc.) from noisy seismic images. We extract the edge images of input images by Canny algorithm as masks to generate the input data with double channels, which improves the amplitude preservation and reduces the interference of noises. The residual groups containing Swin-Transformer blocks and residual connections consist of the backbone of SIST, which extract the global features in a window with preset size and decrease computational cost meanwhile. The pixel shuffle layers are used to up-sample the output feature maps from the backbone to improve the edges, meanwhile up-sampling the input data through a skip connection to enhance the amplitude preservation of the final images especially for clarifying weak events. 3-dimensional synthetic seismic volumes with complex geological structures are created, and the amplitudes of half of the volumes are mixtures of strong and weak, then select 2-dimensional slices randomly to generate training datasets which fits field data well to perform supervised learning. Both numerical tests on synthetic and field data in different exploration regions demonstrate the feasibility of our method.