Abstract: In order to solve the problem of multiplicative noise interference in MＲS signal，a parameter extraction
method based on Markov chain Monte Carlo is proposed． The priori information model and likelihood function
model of the complex envelope parameters from ground magnetic resonance ( MＲS: Magnetic Ｒesonance
Sounding) is established． A novel method based on MCMC( Markov Chain Monte Carlo) ，which sample and fit
the posterior distribution of the parameters is used to get the data with the most occurrence times of the posterior
distribution． The largest weight of the posterior distribution is used as the optimal estimation value of the
parameters． By comparing the extraction results of MCMC parameters under different noise conditions with the
nonlinear fitting method，it is proved that MCMC method can extract MＲS signal parameters with high accuracy
and stability，which is under the interference of multiplicative noise．

Key words: Markov chain, Monte Carlo, nonlinear fitting, bayesian theorem