Seismic Impedance Inversion Based on Residual Attention Network

Abstract

Listen

Translate article

Deep learning 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 3D 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, deep learning has set the state-of-the-art in many applications across wide range of scientific and engineering disciplines. In this paper, we present Residual Attention Network (ResANet), a CNN incorporating with residual modules and two attention mechanisms: channel-wise attention and feature-map attention, for seismic impedance inversion. The proposed network can fuse multi-scale channel information and recalibrate channel-wise 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. 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 compare with three closely related networks. The accuracy of ResANet is improved by 1 to 2 orders of magnitude on the 6 well logs provided in field dataset tests compare with commercial software (InverTrace Plus module in Jason) using Constrained Sparse Spike Inversion (CSSI) method.