The superconducting electromagnetic detection is highly susceptible to interference from environmental electromagnetic noise, which seriously affects the accuracy of data interpretation. To address this issue,this paper proposes a superconducting electromagnetic noise suppression method based on the principle of signal-to-noise separation and denoising. Firstly, the FastICA algorithm is utilized to extract the secondary magnetic field signal from the observed signal. Then, an optimization constraint model is formulated, which is based on maximizing non-gaussianity and minimizing distortion to address the uncertainty of the signal amplitude after separation. Finally, the CS search algorithm is used to iteratively solve for the optimal parameters of the separation matrix. Simulation experiment results show that the proposed method outperforms the PCA, WA and ICA methods in terms of signal-to-noise ratio and mean square error metrics, with a 16. 6 dB improvement in signal-to-noise ratio after noise reduction. Field experiment results show that the proposed method has good suppression effects on various environmental electromagnetic noise. The effective signal duration is increased by nearly 4 times after denoising. The observed signal quality is significantly improved, and the imaging interpretation depth reaches 1 000 m. The effectiveness and practicality of the proposed method is fully evaluated.