基于 CS-FastICA 的超导电磁数据消噪方法研究

超导电磁探测极易受到环境电磁噪声的干扰,严重影响了数据的解释精度。针对这一问题,提出了一种基于信噪分离消噪原理的超导电磁噪声抑制方法。该方法首先利用FastICA算法从观测信号中分离提取二次磁场信号,然后针对信号分离后的幅值不确定性问题,基于最大化非高斯性和最小畸变准则构建优化约束模型,最后利用CS搜索算法迭代求解分离矩阵的最优参数值。仿真结果显示,本文方法在信噪比和均方误差指标上均优于PCA、WA和ICA方法,消噪后信号的信噪比提高了16.6 dB。现场实验表明,本文方法对多种环境电磁噪声均具有良好的抑制效果,消噪后观测信有效时间提高了近4倍。观测信号质量得到了显著改善,视电阻率成像解释深度达1 000 m,充分验证了本文方法的有效性和实用性。

Research on the superconducting electromagnetic denoising method based on CS-FastICA

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 electromagneticnoise suppression method based on the principle of signal-to-noise separation and denoising. Firstly, the FastICA algorithm is utilized toextract the secondary magnetic field signal from the observed signal. Then, an optimization constraint model is formulated, which isbased 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 experimentresults show that the proposed method outperforms the PCA, WA and ICA methods in terms of signal-to-noise ratio and mean square errormetrics, with a 16. 6 dB improvement in signal-to-noise ratio after noise reduction. Field experiment results show that the proposedmethod has good suppression effects on various environmental electromagnetic noise. The effective signal duration is increased by nearly4 times after denoising. The observed signal quality is significantly improved, and the imaging interpretation depth reaches 1 000 m. Theeffectiveness and practicality of the proposed method is fully evaluated.