Random noise may accompany the acquisition of seismic signals, and some denoising algorithms can smoothen some of the details in seismic signals, which will result in reduction in seismic data accuracy. In this study, a seismic signal denoising algorithm based on region segmentation gradient histogram preservation (SGHP) is proposed. The proposed algorithm first divides the seismic noise signal into several regions, then estimates the reference gradient histogram for each region. Finally, each region is denoised using gradient histogram preservation so that the gradient distribution of the denoised seismic signal is as close as possible to that of the original signal, achieving the purpose of protecting the details of the seismic signal. SGHP is used to denoise the synthesized seismic signals and post-stack land seismic signals, and is compared with non-local mean filtering (NLM), block matching 3D (BM3D) cooperative filtering, and clustering sparse representation (CSR) algorithms for denoising effect through evaluation indicators such as peak signal to noise ratio and structural similarities. Results show that SGHP has an optimal denoising effect.