Reflection Coefficients Inversion Based on the Bidirectional Long Short-Term Memory Network

Improving the vertical resolution of seismic data to satisfy the demands for detailed characterization of reservoirs is an important part of seismic data processing. The sparse spike inversion (SSI) technique greatly enhances the seismic resolution by assuming that the reflection coefficients follow the sparse distributions. However, its characterization for the spatial structure of thin and thin interbed reservoirs is still limited. In this letter, we propose a novel approach for enhancing seismic resolution using a bidirectional long short-term memory (BiLSTM) neural network by extracting reflection coefficients directly from seismic data. By designing the network architectures, multiple seismic samples map to the specific reflection coefficient. Compared with the SSI method, the BiLSTM neural network provides higher-resolution inversion results on model data, which demonstrates the effectiveness of the proposed approach for thin structure characterizations. The convergence speed of the proposed method is fast and the training process is stable. In addition, we conduct high-resolution seismic data processing on the field data based on the transfer learning technology. High-resolution processing results illustrate the generalization ability and adaptability of the BiLSTM neural network.