Complex seismic data sets benefit from adaptive sparse transformation methods to improve resolution and fidelity. The data-driven tight frame (DDTF) is a fast dictionary learning method that can simplify the sparse representation process and efficiently update the compact frame. However, the DDTF-based method is computationally intensive and relatively ineffective in accurately characterizing high-dimensional data sets with weak features and high noise levels. A new adaptive patch-selection method based on a simple framework for the contrastive learning of visual representations (SimCLR) is designed to further accelerate and improve the dictionary training process in the DDTF. The variance of all of the available patches is first calculated; then, the cosine similarity between the patch with the maximum variance and the other patches is calculated. Finally, the patches with large similarities are used for the filter bank training process. This method can efficiently track seismic events. The efficacy of applying the trained filter bank for seismic data recovery is tested by using the Monte Carlo-DDTF and SimCLR-DDTF methods. The numerical results demonstrate that the SimCLR-DDTF method can intelligently select training patches with more effective information, greatly improve the training efficiency of the DDTF, and obtain high-quality denoising and interpolation results.