SEISMIC IMPEDANCE INVERSION BASED ON RESIDUAL ATTENTION NETWORK

Deep learning has achieved promising results in predicting impedance inversion from seismic data. The volume of seismic data, especially 3D seismic data, is very large. Therefore, it is particularly important to improve the accuracy while ensuring the model efficiency for practicability and follow-up research. In this paper, we present Residual Attention Net (ResANet), a CNN 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, we adopt grouped convolution to improve the computation. Marmousi2 model test results show that our network outperforms several state-of-the-art neural network models in accuracy and stability with superior efficiency for seismic data impedance inversion.