Predicting Subsurface Layer Thickness and Seismic Wave Velocity Using Deep Learning: Knowledge Distillation Approach

Seismic interpretation is a crucial task in geophysics, requiring accurate prediction of subsurface layer thickness and seismic wave velocity. Traditional methods are computationally intensive and often hindered by noise in seismic data. Deep learning offers a promising solution to analyze complex geographical structures, but its computational complexity can be a barrier for deployment. This study introduces a deep learning-based approach enhanced by knowledge distillation (KD) to predict subsurface layer thickness and seismic wave velocity. By leveraging pre-trained convolutional neural networks (CNNs) and Transformers as teacher models, we transfer knowledge to smaller, more efficient student models through multiple knowledge distillation strategies, including cross-architecture, multi-teacher, and self-distillation. Implementing knowledge distillation from a Vision Transformer (ViT) teacher to CNN student models enhances the performance of student models in depth and velocity predictions compared to baseline CNN models without knowledge distillation. Aggregating knowledge from multiple teacher models improves model generalization and reduces overfitting, as evidenced by a decreased gap between training and validation losses. Self-distillation significantly enhanced the performance of simpler architectures like VGG19, achieving over 95% accuracy in several depth and velocity prediction tasks. Our approach was validated using simulated velocity model data, demonstrating that distilling knowledge from deeper, complex models into smaller ones maintains high prediction accuracy while reducing computational requirements. These findings emphasize the efficiency of knowledge distillation in deploying deep learning models in resource-constrained environments. Integrating deep learning with knowledge distillation techniques not only enhances the accuracy of seismic interpretation models but also makes them more feasible for practical applications, offering a powerful tool for seismic exploration and potentially transforming how seismic data is processed for subsurface characterization and accelerating decision-making in exploration and production.