Seismic impedance inversion using a multi-input neural network with a two-step training strategy

Deep learning has shown excellent performance in simulating complex nonlinear mappings from the seismic data to elastic parameters. However, seismic acoustic impedance estimated from a direct mapping from seismic waveform data to P-wave impedance (single-input network) is hampered by the limited frequency bands. In this paper, we propose to incorporate the low-frequency impedance model to constrain the inversion (multi-input network). We add a feature fusion layer to force the lateral smoothness. Besides, usually, a given seismic survey is likely to contain only a few well logs, which is insufficient for conventional deep-learning -based methods to learn the complex mapping from seismic data to elastic parameters. The problem is compounded by the fact that a network trained with synthetic data (compensated for the lack of logs) cannot be directly used for field data. Therefore, we propose to use transfer learning to mitigate this issue. The multi-input neural network is trained using synthetics and real data in two stages. We carry out experiments to demonstrate that the two-step training multi-input network approach has high accuracy in the time direction, excellent continuity in the lateral direction and favourable robustness. Synthetic and field data examples demonstrate that the proposed network can accurately predict impedance even with limited logging data, which provides a reference for oil and gas exploration in the actual production process.