Inception–Attention–BiLSTM Hybrid Network: A Novel Approach for Shear Wave Velocity Prediction Utilizing Well Logging

Shear wave velocity prediction is critical for applications in petrophysics, reservoir characterization, and unconventional energy resource development. While empirical formulas and theoretical rock physics models offer solutions, they are often limited by geological complexity, high cost, and computational inefficiency. After the emergence of deep learning methods, a series of new approaches have been provided to tackle these problems. In this study, a novel Inception–attention–BiLSTM hybrid network is proposed to enhance shear wave prediction accuracy and stability by integrating the strengths of three components: Inception for multi-scale feature extraction, attention mechanisms for dynamically highlighting key temporal features, and BiLSTM for capturing long-term dependencies in logging data. The test dataset of this network comes from the Jurassic Badaowan Formation in the Junggar Basin, achieving superior performance compared to standalone Inception and BiLSTM networks. The proposed hybrid network demonstrated MAE and R2 values of 0.211 and 0.994, respectively, outperforming Inception (MAE 0.671, R2 0.981) and BiLSTM (MAE 0.215, R2 0.991). These results underscore its robustness in handling complex logging data, providing a more accurate and generalizable framework for Vs prediction while addressing limitations of traditional methods. This work highlights the potential of hybrid deep learning architectures in advancing logging data analysis and reservoir characterization.