VSP Wavefield Separation via Physical Prior-guided Convolutional Autoencoder

Conventional vertical seismic profiling (VSP) wavefield separation techniques often struggle to achieve high-accuracy results in practical applications due to three key factors: uncertainties in first-break estimation, the non-stationary characteristics of time-variant wavelets, and the influence of formation dip angles on wave propagation patterns. Most existing methods, including traditional filtering-based techniques (e.g., median filtering) and deep learning-based wavefield separation approaches, heavily rely on high-quality velocity models or training datasets, which limits their applicability and separation accuracy.Therefore, this paper proposes a physical prior-guided VSP wavefield separation method. Specifically, we choose physical prior information to guide the training of the deep learning model. We utilize the Curvelet transform and gradient structure tensor (GST) to extract physical prior information from VSP data. Combined with a dual-convolutional autoencoder model, this method effectively separates upgoing and downgoing waves. Experimental results on both synthetic and real data demonstrate that the proposed method outperforms traditional separation techniques, effectively reduces pseudo-axis effects, prevents energy leakage, and significantly improves the accuracy and robustness of wavefield separation.