Abstract：

　　Low-rank matrix factorization is a kind of machine learning algorithm. In recent years, the algorithm has received extensive attention in the problem of seismic data reconstruction. Much research related to model building and numerical calculations has been published. However, the exact solution of low-rank matrix factorization requires the regularization parameters, and the regularization parameters are directly related to the statistical parameters such as the mean and variance of the decomposed seismic data. But these parameters cannot be obtained precisely because of missing data and random noise. In order to solve this problem, this paper introduces the Bayesian probabilistic matrix factorization algorithm, which simulates the mean and variance randomly and calculates the optimal reconstruction result by calculating the probability density function. Synthetic seismic data and real seismic data tests indicate that the proposed method could improve the accuracy and stability of seismic data reconstruction.

Key words： data reconstruction machine learning low-rank matrix factorization Bayes’s theorem Markov chain Monte Carlo

Received： 04 April 2018

Corresponding Authors：LI Xiangyang, xyl1962@hotmail.com

Cite this article：HOU Sian,ZHANG Feng,LI Xiangyang. Seismic data reconstruction via a Bayesian probabilistic matrix factorization algorithm[J]. Petroleum Science Bulletin, 2018, 3(2): 154-166.

URL：