A highly efficient adaptive geomagnetic signal filtering approach using CEEMDAN and salp swarm algorithm

Convenient and helpful defect information within the magnetic field signals of an energy pipeline is often disrupted by external random noises due to its weak nature. Non-destructive testing methods must be developed to accurately and robustly denoise the multi-dimensional magnetic field data of a buried pipeline. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is an innovative technique for decomposing signals, showcasing excellent noise reduction capabilities. The efficacy of its filtration process depends on two variables, namely the level of additional noise and the number of ensemble trials. To address this issue, this paper introduces an adaptive geomagnetic signal filtering approach by leveraging the capabilities of both CEEMDAN and the salp swarm algorithm (SSA). CEEMDAN generates a sequence of intrinsic mode functions (IMFs) from the measured geomagnetic signal based on its initial parameters. The Hurst exponent is then applied to distinguish signal IMFs and reproduce the primary filtered signal. SSA fitness, representing its peak value (excluding the zero point) in the normalized autocorrelation function, is utilized. Ultimately, optimal parameters that maximize fitness are determined, leading to the acquisition of their corresponding filtered signal. Comparative tests conducted on multiple simulated signal variants, incorporating varied levels of background noise, indicate that the efficacy of the proposed technique surpasses both EMD denoising strategies and conventional CEEMDAN approaches in terms of signal-to-noise ratio (SNR) and root mean square error (RMSE) assessments. Field testing on the buried energy pipeline is performed to showcase the proposed method's ability to filter geomagnetic signals, evaluated using the detrended fluctuation analysis (DFA).