  Recent progress in the use of deep learning for Full Waveform Inversion (FWI)
has demonstrated the advantage of data-driven methods over traditional
physics-based approaches in terms of reconstruction accuracy and computational
efficiency. However, due to high computational complexity and large memory
consumption, the reconstruction of 3D high-resolution velocity maps via deep
networks is still a great challenge. In this paper, we present InversionNet3D,
an efficient and scalable encoder-decoder network for 3D FWI. The proposed
method employs group convolution in the encoder to establish an effective
hierarchy for learning information from multiple sources while cutting down
unnecessary parameters and operations at the same time. The introduction of
invertible layers further reduces the memory consumption of intermediate
features during training and thus enables the development of deeper networks
with more layers and higher capacity as required by different application
scenarios. Experiments on the 3D Kimberlina dataset demonstrate that
InversionNet3D achieves state-of-the-art reconstruction performance with lower
computational cost and lower memory footprint compared to the baseline.
