A certain amount of noise would be introduced by a dictionary update step using the K-means sequential generalized (SGK) denoising algorithm. To reduce the effect of noise interference on dictionary atoms, a seismic signal denoising algorithm is proposed based on controlled interference SGK (C-SGK) dictionary learning under a compressive sensing framework. The algorithm compares the signal-to-noise ratio and the threshold set in the dictionary update step, which determines whether to update the atom: the atoms should be sequentially updated only if the signal-to-noise ratio is greater than the threshold. The experimental results of synthesized and real seismic signal denoising in this study indicate that the proposed algorithm can effectively control noise interference. Compared with traditional SGK denoising, the proposed algorithm demonstrates a better denoising effect on seismic signals.