Random noise often exists in seismic data acquisition, which will affect the accuracy of seismic data analysis. For Gaussian noise existing in seismic data, traditional non-local mean denoising algorithms cannot effectively maintain the in-phase axis edge in seismic data after denoising the seismic data. The total variation regularization non-local mean algorithm is applied to seismic data noise reduction. By calculating the noise estimation value and then updating the weight value of the de-jitter non- local mean algorithm, the de-jitter non-local mean noise reduction result is subjected to the total variation regularization constraint so as to obtain the best seismic data noise reduction result. While Gaussian noise is effectively removed, the edge of the in-phase axis of seismic data is retained. Through noise reduction experiments on synthetic seismic data, offshore pre-stack seismic data and onshore post-stack seismic data, the peak signal-to-noise ratio, mean square error and average structural similarity of the algorithm are compared with non-local mean algorithm and non-local mean algorithm based on neighbor selection strategy. It is concluded that the total variation regularization non-local mean noise reduction algorithm can effectively reduce noise while retaining the in-phase axis edge details of seismic data.