1. College of Geophysics, Chengdu University of Technology, Chengdu 610059, China 2. Sichuan Geophysical Survey and Research Institute, Chengdu 610072, China
In practical detection operations using ground-penetrating radar (GPR), factors such as environmental noise and instrument errors frequently cause signals to be mixed with substantial noise, seriously reducing signal quality and the reliability of analytical results. To address this issue, this study proposed a time-frequency peak filtering method combined with minimum mean cross-entropy (TFPF-MMCE) for denoising GPR signals. This method combined time-frequency peak filtering with the cross-entropy function, enabling effective noise suppression and precise preservation of valid signals through precise optimization of the time-frequency representation, thereby significantly improving the quality of GPR signals. Numerical simulation and field GPR experiments validated that the TFPF-MMCE method exhibited a high noise removal capability and, thus, can effectively eliminate random noise while significantly improving signal clarity and reliability. Compared to traditional denoising methods, TFPF-MMCE shows significant advantages in denoising effectiveness and noise resistance stability, suggesting promising application potential and practical value in the field of GPR signal processing.
Yang L. Analysis of interference characteristics of ground penetrating radar railway sleepers[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2024, 46(4):453-461.
Zhang S W, Wu R X, Han Z A, et al. The application of bilateral filtering to denoise processing of ground penetrating radar data[J]. Geophysical and Geochemical Exploration, 2021, 45(2):496-501.
Zhang X W, Gao Y Z, Fang G Y. Application of generalized S transform with lowpass filtering to layer recognition of Ground Penetrating Radar[J]. Chinese Journal of Geophysics, 2013, 56(1):309-316.
Huang M, Zhu D B, Guo Z X, et al. Research on the application of continuous wavelet transform in gpr signal analysis[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2012, 34(5):593-598,503.
[6]
Javadi M, Ghasemzadeh H. Wavelet analysis for ground penetrating radar applications:A case study[J]. Journal of Geophysics and Engineering, 2017, 14(5):1189-1202.
[7]
Xu J C, Ren Q, Shen Z Z. Ground-penetrating radar time-frequency analysis method based on synchrosqueezing wavelet transformation[J]. Journal of Vibroengineering, 2016, 18:315-323.
Wu N, Wu J, Wu Z J. Research on denoising of ground penetrating radar signals using the time-frequency spectral decomposition reassignment algorithm[J]. Geophysical and Geochemical Exploration, 2018, 42(1):220-224.
Yu S W, Niu G, Qin H, et al. Time and frequency analysis of GPR data for tunnel geological forecast of Karst caves[J]. Geotechnical Investigation & Surveying, 2023, 51(10):67-72.
[10]
Li J, Zhao X L, Cheng H, et al. Data augmentation and denoising of magnetotelluric signals based on CS-ResNet[J]. Geophysics, 2025, 90(3):WA31-WA46.
Li Y, Yang B J, Lin H B, et al. Suppression of strong random noise in seismic data by using time-frequency peak filtering[J]. Scientia Sinica:Terrae, 2013, 43(7):1123-1131.
Lin H B, Ma H T, Li Y, et al. Elimination of seismic random noise based on the SW statistic adaptive TFPF[J]. Chinese Journal of Geophysics, 2015, 58(12):4559-4567.
[13]
Liu Y, Peng Z, Wang Y, et al. Seismic noise attenuation by time-frequency peak filtering based on Born-Jordan distribution[J]. Journal of Seismic Exploration, 2018, 27(6):557-575.
[14]
Liu N H, Yang Y, Li Z, et al. Seismic signal de-noising using time-frequency peak filtering based on empirical wavelet transform[J]. Acta Geophysica, 2020, 68(2):425-434.
[15]
Boashash B, Mesbah M. Signal enhancement by time-frequency peak filtering[J]. IEEE Transactions on Signal Processing, 2004, 52(4):929-937.
[16]
Loughlin P, Pitton J, Hannaford B. Approximating time-frequency density functions via optimal combinations of spectrograms[J]. IEEE Signal Processing Letters, 1994, 1(12):199-202.
[17]
Groutage D. A fast algorithm for computing minimum cross-entropy positive time-frequency distributions[J]. IEEE Transactions on Signal Processing, 1997, 45(8):1954-1970.
[18]
Shah S I, Loughlin P J, Chaparro L F, et al. Informative priors for minimum cross-entropy positive time-frequency distributions[J]. IEEE Signal Processing Letters, 1997, 4(6):176-177.
[19]
Aviyente S, Williams W J. Minimum entropy time-frequency distributions[J]. IEEE Signal Processing Letters, 2005, 12(1):37-40.
[20]
Moca V V, Bârzan H, Nagy-Dăbâcan A, et al. Time-frequency super-resolution with superlets[J]. Nature Communications, 2021, 12(1):337.
doi: 10.1038/s41467-020-20539-9
pmid: 33436585