Research on FID signal denoising method in proton precession magnetometer using adaptive local iterative filtering

The proton precession type magnetometers serve as fundamental equipment for geophysical magnetic exploration and stand out as one of the most accurate magnetometers widely utilized in magnetic surveys. However, factors such as magnetic field gradients, background electrical noise, and instrument stability can contribute to a reduction in the signal-to-noise ratio (SNR) of the Free Induction Decay (FID) signal of the magnetometer. To enhance the magnetic field measurement accuracy of the magnetometer, the Adaptive Local Iterative Filtering (ALIF) algorithm is applied for noise reduction in the FID signal. The ALIF algorithm is founded on iterative decomposition and employs the fundamental solution system of the Fokker-Planck equation to construct the filtering function, demonstrating adaptive characteristics. Proper parameter selection and accurate intrinsic mode function components can be obtained and widely applied. This paper validates the noise suppression effect of the ALIF algorithm on the FID signal through parameter simulation experiments. The experimental results indicate that, in comparison with evaluation metrics such as the SNR and correlation number of band-pass filters, the ALIF algorithm can effectively suppress various noises in the FID signal.