Denoising of Seismoelectric Logging Signal Based on Stochastic Resonance

Seismoelectric well logging is a new geophysical logging technique based on acoustic and electric conversion of double electric layers in porous media to measure permeability parameters of reservoir. However, due to the complexity of pore structure in the reservoir, the efficiency of acoustic-electrical conversion is very low. Coupled with the influence of environmental noise, the signal-to-noise ratio of the received seismicelectric signal is very weak, which brings great challenges to the interpretation of data from the observation of electromagnetic data to the reservoir porosity parameters. This is also the main reason why it has not been applied in practice so far. Therefore, stochastic resonance theory is introduced in this paper, and a nonlinear bistable stochastic resonance system based on Langevin equation is constructed to reduce the noise and improve the quality of seismoelectric logging signal. By constructing the optimization problem of the output signal-to-noise ratio of the system as the cost function, the maximum signal-to-noise ratio is obtained by the genetic algorithm based on global optimization, and the comparison experiment is carried out with the traditional filtering method. The experimental results show that the method based on stochastic resonance can significantly improve the signal-to-noise ratio of seismoelectric logging signals, which provides a strong technical support for the application of seismoelectric logging technology.