Denoising method of ground-penetrating radar signal based on independent component analysis with multifractal spectrum

•A novel method for denoising ground penetrating radar (GPR) data based on fractal methods.•Independent component analysis based on fourth-order accumulation is used to separate the effective and noise signals from the GPR measured signal.•The multifractal spectrum is used to solve the disorder problem of separated signals.•The entire denoising process can be implemented automatically, which is more suitable for processing massive GPR scan data. Denoising is commonly used to improve the signal-to-noise ratio of ground-penetrating radar (GPR) for target detection and recognition. We propose an adaptive GPR denoising method based on the fast independent component analysis (FastICA) with wavelet transform modulus maxima (WTMM) multifractal spectrum, which can effectively separate the information of the abnormal body in the reservoir that is submerged by the noise signal. The target and background noise signals are extracted with FastICA and identified with multifractal spectra. The numerical example shows that FastICA can effectively separate the components of the target signal and noise signal when the useless signals submerge the target signal. The WTMM multifractal spectrum can solve the disorder problem in the results of FastICA. The results based on the field measured data show that the proposed denoising method has higher stability and convenience than the traditional methods, which validates the effectiveness of the proposed method.