Physical Model and Super-Resolution Theory-Guided Unsupervised Deep Learning Deconvolution for Seismic Resolution Enhancement

Seismic wavelet interference limits the vertical resolution of seismic data, making it challenging to accurately characterize subsurface geological structures. Seismic reflectivity estimation is a key process for improving the vertical resolution of seismic data. Deep learning-based seismic reflectivity estimation methods typically rely on labels generated from well data, which can be costly to obtain. Moreover, seismic reflectivity estimation is an ill-posed problem, meaning that while the estimated seismic reflectivity may fit the observed data, it can still differ significantly from the true reflectivity, particularly concerning thin layers. To address these challenges, we propose an unsupervised deep learning deconvolution framework guided by a physical convolution model and super-resolution mathematical theory. The network is trained using a combination of reconstruction loss, position prior loss, and sparse loss. Specifically, reconstruction loss establishes a closed-loop connection between the low-resolution seismic data and the estimated seismic reflectivity using the Robinson convolution model, eliminating the need for labels in training. Position prior loss estimates the seismic reflectivity position based on the super-resolution mathematical theory, which is particularly effective for thin layers, improving the interpretability and accuracy of the seismic reflectivity estimation. Sparse loss, based on the <tex-math notation="LaTeX">$L1$ </tex-math> norm, enforces sparsity in the seismic reflectivity estimation, enhancing its stability. Both synthetic and field data examples demonstrate that the proposed method outperforms conventional sparse-spike deconvolution method, providing better thin-layer seismic reflectivity estimates and improved lateral continuity.