Research And Application Of Improved Particle Filter Algorithm For Seismic Random Noise Suppression

Seismic exploration is an important detection method for oil and gas and mineral resources.Due to the complexity of the exploration environment,the acquired seismic data often contain high noise,which brings great difficulties to data processing,analysis and imaging.Suppressing random interference is an effective method to improve signal-to-noise ratio and data quality.At present,the goal of the country's oil and gas exploration has been developed in the direction of "two widths and one high".In the process of processing traditional random noise suppression technology,it often interferes with non-stationary and effective signals with spatial and temporal characteristics.Therefore,the development of an efficient random noise suppression technique is particularly important.In this paper,a nonlinear filtering method-particle filter is used to suppress random seismic noise.Particle filtering combines recursive Bayesian estimation and Monte Carlo thought,which can get rid of the constraints of system model and noise distribution,and has unique advantages in solving nonlinear filtering problems.However,due to the problem of particle degradation and lack of particle diversity in the particle filtering method,the number of particles containing effective seismic information is small,and the geological information contained is incomplete,resulting in the deviation of the filtering results from the real signal,making the filtering effect unsatisfactory.In order to solve the degradation of particles,enhance the diversity of particles,improve the quality of particles,make particles approach the distribution of real signals,and make particles contain more effective underground structure information,this paper optimizes the particle filter algorithm.In this paper,particle filter is optimized and applied to suppress the random noise of seismic data.First,the method establishes a system model of seismic data,and predicts and updates the Bayesian estimation.Then,uses the firefly algorithm to modify the particle filter.The basic idea of this method is: through iterative optimization,the particles located at the end of the probability distribution are moved to the highlikelihood region,so that the particle set has a better coverage of the high-likelihood region,thereby improving the overall quality of the particles and making more particles.Effective seismic signals can be obtained,which can not only solve the problem of particle degradation,but also maintain the diversity of particles,thereby improving the filtering effect.Finally,the improved particle filter and the traditional filtering method are applied to the single-channel convolution model and the complex forward model at the same time.The results show that the improved particle filter has stronger noise suppression ability.Finally,by comparing with the traditional algorithm in the processing of actual seismic data,it is verified that the method has good useful signal protection ability and noise suppression performance.