A Fully Parameter Adaptive Magnetic Anomaly Detection Method Based on Multistable Stochastic Resonance System

Magnetic anomaly detection (MAD) often encounters high false alarm rates or detection failures due to the rapid decay of magnetic anomaly data with distance and its susceptibility to environmental interference. A fully parameter-adaptive MAD method based on a multistable stochastic resonance (SR) system is proposed to address this issue. This multistable SR model exhibits higher noise utilization than monostable and bistable models, demonstrating superior detection performance for weak signals under strong background noise. However, as the complexity of the well function increases, the severe coupling of system parameters complicates the adjustment of the potential well function. This study proposes an adaptive parameter selection method, which transforms the signal into the frequency domain using continuous wavelet transform (CWT). We propose a combined fitness function comprising peak signal-to-noise ratio (PSNR) and kurtosis index, calculated between the 2-D frequency-domain image of the magnetic anomaly signal and the reference frequency-domain image. Particle swarm optimization (PSO) is then employed to optimize the system parameters, achieving optimal alignment between the multistable SR system and the input signal, improving the detection probability. The experimental section tests the proposed method on synthetic and field data and compares it to traditional detection methods. The results indicated that the method maintains a high detection probability when the signal-to-noise ratio (SNR) is below −10 dB with colored noise condition and outperforms traditional methods in detection performance.