A Signal Detection Method for Seismoelectric Logging Based on Stochastic Resonance

To overcome the limitations of traditional stochastic resonance methods in high-frequency signal processing, the scale transformation of phase trajectory time was introduced to improve the dynamic equations of the nonlinear bistable stochastic resonance system. Through numerical simulation and circuit design, the practicality of the system was enhanced, and time-domain and frequency-domain waveform analysis of the numerical simulation model and circuit output signals was conducted. With the output signal-to-noise ratio as the evaluation function, a genetic algorithm was employed to optimize system parameters and obtain the optimal output. A bistable stochastic resonance system based on the scale transformation of phase trajectory time was constructed and applied to seismoelectric logging signals. The results show that the signal-to-noise ratio of the stochastic resonance system’s output signal has improved by 23.5642 dB, and the amplitude at the output signal’s characteristic frequency is 44 times that of traditional linear filtering techniques. The bistable stochastic resonance system based on scale transformation of phase trajectory time can directly process high-frequency seismoelectric logging signals, reduce noise in the signals, and significantly improve signal clarity and quality. This system can overcome the limitations of small parameters and achieve successful detection of seismoelectric logging, providing a new solution for extracting weak signals from oil wells in complex environments.