Deep learning (DL) has achieved promising results for impedance inversion via seismic data. Generally, these networks, composed of convolution layers and residual blocks, tend to deliver good results with deep architectures. Nevertheless, deep networks accompany a large number of parameters and longer training time. The volume of seismic data, especially 3-D scenarios, is very large. Therefore, it is particularly important to improve the accuracy while ensuring the model efficiency for practical implementation. With the flourishing new modules and techniques, DL has set the state of the art in many applications across a wide range of scientific and engineering disciplines. In this article, we present a residual attention network (ResANet), a convolutional neural network (CNN) incorporating with residual modules, and two attention mechanisms: channelwise attention and feature-map attention, for seismic impedance inversion. The proposed network can fuse multiscale channel information and recalibrate channelwise feature responses as well as receptive fields adaptively. At the same time, ResANet adopts grouped convolution, dilated convolution, and dropout techniques to improve the computation efficiency and stability. The Marmousi2 synthetic model and field data test results show that the proposed network outperforms several comparable neural networks in accuracy and generalization ability while ensuring efficiency for seismic data impedance inversion. For the field data test, transfer learning is also evoked to further improve the performance. ResANet tends to predict impedance with high resolution and strong lateral continuity compared with three closely related networks. The accuracy of ResANet is improved by 1–2 orders of magnitude on the six well logs provided in field dataset tests compared with commercial software (InverTrace Plus module in Jason) using the constrained sparse spike inversion (CSSI) method.

中文翻译：

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基于剩余注意力网络的地震阻抗反演

深度学习 (DL) 在通过地震数据进行阻抗反演方面取得了可喜的成果。通常，这些由卷积层和残差块组成的网络倾向于通过深度架构提供良好的结果。然而，深度网络伴随着大量的参数和更长的训练时间。地震数据量，尤其是 3-D 场景，非常庞大。因此，在保证模型效率的同时提高准确率对于实际实施尤为重要。借助蓬勃发展的新模块和技术，DL 在广泛的科学和工程学科的许多应用中树立了最先进的技术。在本文中，我们提出了一个残差注意力网络 (ResANet)、一个包含残差模块的卷积神经网络 (CNN) 和两种注意力机制：通道注意和特征图注意，用于地震阻抗反演。所提出的网络可以融合多尺度通道信息并自适应地重新校准通道特征响应以及感受野。同时，ResANet 采用分组卷积、空洞卷积和 dropout 技术来提高计算效率和稳定性。Marmousi2 合成模型和现场数据测试结果表明，在保证地震数据阻抗反演效率的同时，所提出的网络在准确性和泛化能力方面优于几个可比较的神经网络。对于现场数据测试，还引发了迁移学习以进一步提高性能。与三个密切相关的网络相比，ResANet 倾向于以高分辨率和强横向连续性来预测阻抗。