Skip to main content
SpringerLink
Log in

The study of time domain denoising for the time-frequency electromagnetic method prospecting data

  • Published:
Applied Geophysics Aims and scope Submit manuscript

Abstract

The time-frequency electromagnetic prospecting system has both the characteristics of frequency sounding and transient electromagnetic sounding system, can obtain apparent resistivity both in frequency domain and time domain, therefore, thus, more accurate and credible geoelectric information can be obtained. After analyzing, there are mainly three typical types of noise in time-frequency electromagnetic observed data, they are 50Hz power frequency and its harmonics interference noise, high-frequency random impulse noise and low frequency interference noise. In view of characteristics of these three types of noise and the time-frequency electromagnetic prospecting signal, we adopted frequency domain band stop filtering method remove the 50 Hz power frequency and its harmonics interference noise, proposed a segmentation and extension median filtering method and a fitting fixed extreme EMD method to remove the high frequency random impulse noise and the low frequency interference noise separately, and proposed a median filtering window size test selection method based on variance and skewness coefficient of sample signal. Moreover, we designed a denoising processing fl ow as handle 50Hz power frequency and its harmonics interference noise at fist step, handle high frequency random impulse noise at second step, handle low frequency interference noise at last step, it can give full advantages of the denoising handling methods. By theoretical analysis and verification by practical observation data experiments, the methods what we adopted and proposed and the processing flow we designed can effectively remove the three types of interference noises effectively and avoid method error, can preserve phase and amplitude information of effective signal accurately, finally, high quality and satisfactory time domain signal waveform can obtained as a result, and lays a good foundation for extracting accurate transient electromagnetic attenuation curve in time domain for the Time-Frequency electromagnetic prospecting data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • An, H. B., and Shen, Y. Y., 2005, A method to suppress narrow-band interference based on frequeney domain filter: Optoelectronic Technology & Information, 18(5), 64–68.

    Google Scholar 

  • Andrew, K. H., 1995, Application of frequency-domain polyphase filtering to quadrature sampling: Proc Spie, 2563, 450–457.

    Article  Google Scholar 

  • Bai, F. F., Miao, C. Y., Zhang, C., and Gan, J. M., 2010, Studying on denoising algorithm of heart sound signal based on the generalized mathematical morphology:IEEE International Conference on Signal Processing, 1797–1800.

    Google Scholar 

  • Bhusana, P., Jumlong, V., and Christofer, T., 2013, Noise reduction for nonlinear nonstationary time series data using averaging intrinsic mode function: Algorithms, 6, 407–429.

    Article  Google Scholar 

  • Brian, H. T., and Eric, L. M., 2012, Nonlocal means denoising of ECG signals: IEEE Transactions on Biomedical Engineering, 59(9), 2383–2386.

    Article  Google Scholar 

  • Chang, K. M., 2010, Arrhythmia ECG noise reduction by ensemble empirical mode decomposition: sensors, 10, 6063–6080.

    Article  Google Scholar 

  • Cui, Z. H., Cheng, M. H., Wu, Q. L., and Wang, B. Z., 2005, A technique of fast median filtering and its application to data quality control of doppler radar: Plateau Meteorology, 24(5), 727–733.

    Google Scholar 

  • Daniel, N., David, C. F., Vladimir, E., et al., 2000, Denoising electrocardiogram signal using adaptive wavelets: International Eurasip Conference Biosignal, 1–4.

    Google Scholar 

  • Ding, K. L., Sheng, Y. Z., Ou, J. K., 2010, Methods of line-fitting based on total least-squares: Journal of Liaoning Technical University(Natural Science), 29(1), 44–47.

    Google Scholar 

  • Dong, W. B., Zhao, X. M., Liu, F., and Zhao G., 2008, The time-frequency electromagnetic method and its application in western China: Applied Geophysics, 5(2), 127–135.

    Article  Google Scholar 

  • George, T., and Thomas, D. X., 2011, Signal denoising using empirical mode decomposition and higher order statistics: International Journal of Signal Processing, Image Processing and Pattern Recognition, 4(2), 91–106.

    Google Scholar 

  • Gruber, P., Theis, F., Tomé, A., et al, 2004, Automatic denoising using local independent component analysis: Proceedings Ofs.

    Google Scholar 

  • Gyanaprava, M., Kumar, B., and Asit K. M., 2013, Denoising of heart sound signal using wavelet transform: International Journal of Research in Engineering and Technology, 2(4):719–723.

    Article  Google Scholar 

  • He, Z. X., Wang, X. B., Kong, F. S., et.al., 2002, The Time-Frequency electromagnatic sounding method: The annual China geophysical society, Chinese Geophysical Society 18th Annual Conference Proceedings.

    Google Scholar 

  • He, Z. X., 2006, TFEM method(Patent No.ZL03150098.6.2003).

    Google Scholar 

  • He, Z. X., 2005, Mapping reservoir boundary by TFEM technique:one case study:75th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, Houston, 631–634.

    Google Scholar 

  • Huang, N. E., Shen, Z., Long, S. R., et al, 1998, The empirical mode decomposition and the Hilbert spectrum for non-stationary time series analysis: Proceeding: Mathematical, Physical and Engineering Sciences. London: The Royal Society Press, 454(1971):903–995.

    Google Scholar 

  • Jiang, Q. Y., 2010, Study on the key technology of wide field electromagnetic sounding instrument: PhD Thesis, Central South University, Changsha.

    Google Scholar 

  • Kai, S., and Monika D., 2012, Audio denoising by generalized time-frequency thresholding: AES 45TH International Conference, Helsinki, Finland, 1–10.

    Google Scholar 

  • Kalavathy, S., and Suresh, R.M., 2011, A switching weighted adaptive median filter for impulse noise removal: International Journal of Computer Applications, 28(9), 8–13.

    Article  Google Scholar 

  • Li, C. W., Zhan, L. W., and Shen, L. Q., 2015, Friction signal denoising using complete ensemble EMD with adaptive noise and mutual information: Entropy, 17, 5965–5979.

    Article  Google Scholar 

  • Li, G., Xiao, X., Tang, J. T., et al, 2017, Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering: Applied Geophysics, 14(4), 581–590.

    Article  Google Scholar 

  • Li, H. Q., Wang, X. F., and Chen, L., et al., 2014, Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope: Circuits Syst Signal Process, 33, 1261–1276.

    Article  Google Scholar 

  • Li, H., Sun, Y. L., and Xu, B. C., 2007, Noise elimination of PD signals by independent component analysis: Advanced Engineering Sciences, 39(6), 143–148.

    Google Scholar 

  • Liu, L. M., Sun, Y. R., and Li, L., 2004, Research of median f ilter technology: Journal of Yunnan NormalU niversity, 24(1), 23–27.

    Google Scholar 

  • Liu, W., Cao, S. Y., Chen, Y. K., Zu, S. H., 2016, An effective approach to attenuate random noise based on compressive sensing and curvelet transform: Journal of Geophysics and Engineering, 13(2016), 135–145.

    Article  Google Scholar 

  • Macnae, J., Lamontagne, Y., and West, G., 1984, Noise processing techniques for time-domain EM systems:G eophysics, 49(7), 934–948.

    Google Scholar 

  • Mathieu, R., Olivier, J., Christophe, M., and Frederic, B., 2006, Mitigating pulsed interference using frequency domain adaptive filtering: Ion Gnss International Technical Meeting of the Satellite Division of the Institute of Navigation, 2251–2260.

    Google Scholar 

  • Minty, B., and Hovgaard, J., 2002, Reducing noise in gamma-ray spectrometry using spectral component analysis: Exploration Geophysics, 33(4):172–176.

    Article  Google Scholar 

  • Mo, D., Jiang, Q. Y., Li, D. Q., et al, 2017, Controlled- source electromagnetic data processing based on gray system theory and robust estimation: Applied Geophysics, 14(4), 570–580.

    Article  Google Scholar 

  • Nelson, D. J., Cristobal, G., Kober, V., et al, 1999, Denoising using time-frequency and image processing methods: Proceedings of SPIE - The International Society for optics and photonics, 3807, 564–581.

    Google Scholar 

  • Pei, Y., Wu, Y. G., and Jia, D. C., 2012, Research on PD signals denoising based on EMD method: Przeglad Elektrotechniczny(Electrical Review), 88(1), 137–140.

    Google Scholar 

  • Reninger, P. A., Martelet, G., Deparis, J., et al., 2011, Singular value decomposition as a denoising tool for airborne time domain electromagnetic data: Journal of Applied Geophysics, 75(2), 264–276.

    Article  Google Scholar 

  • Reza, S., Mohammad, B., Shamsollahi, et al., 2007, A nonlinear Bayesian filtering framework for ECG denoising: IEEE Transactions on Biomedical Engineering, 54(12), 2172–2185.

    Article  Google Scholar 

  • Rishi, R. S., and Ram, B. P, 2018, Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition: Biomedical Signal Processing and Control, 45, 33–49.

    Article  Google Scholar 

  • Rubana, H. C., Mamun, B. I. R., Mohd, A. B. M. A., et al, 2013, Surface electromyography signal processing and classification techniques: Sensors, 13, 12431–12466.

    Article  Google Scholar 

  • She, L. H., Xu, Z. Q., Zhang, S., et al, 2010, De-noisng Of ECG based on EMD improved-thresholding and mathematical morphology operation: International Conference on Biomedical Engineering & Informatics(3rd BMEI), 2(1), 838–842.

    Google Scholar 

  • Sheng, Z., Xie, Q. S., and Pan, C. Y., 2008, Probability theory and mathematical statistics(Fourth Edition): Higher Education Press, China.

    Google Scholar 

  • Simon P. and Patrick H., 2007, Median Filtering in Constant Time: IEEE Press, 16(9), 2389.

    Google Scholar 

  • Suo, J. Q., 2010, A new improved filtering algorithm based on median filteralgorithm: Msc. Thesis, Beijing University of Posts and Telecommunications, Beijing.

    Google Scholar 

  • Tamer, R., 2004, Adaptive hybrid mean and median filtering of high-ISO long-exposure sensor noise for digital photography: Journal of Electronic Imaging, 13(2), 264–277.

    Article  Google Scholar 

  • Tanusree, G., Debnath, B., Samir, K. B., et al, 2014, A review on different techniques to de-noise a signal: International Journal of Control and Automation, 7(3), 349–358.

    Article  Google Scholar 

  • To, A. C., Moore, J. R., and Glaser, S. D, 2009, Wavelet denoising techniques with applications to experimental geophysical data: Signal Processing, 89(2009), 144–160.

    Article  Google Scholar 

  • Turkey, J. W., 1970, Exploratory data analysis: Reading MA: Addison-Wesley, 212.

    Google Scholar 

  • Wang, H., and Wang, X. H., 2011, An improved adaptive median filter based on noised detection: Journal of China University of Metrology, 22(4), 382–385.

    Google Scholar 

  • Yu, C. X., Wei, W. B., Jing, J. E., et al., 2010, Application of Hilbert-Huang transformation to marine magnetotelluric sounding data processing: Progress in Geophysics, 25(3): 1046–1056.

    Google Scholar 

  • Zhang, B.M., Jiang, Q. Y., Mo, D., et al, 2015, A novel method for handling gross errors in electromagnetic prospecting data: Chinese J.Geophys, 58(6), 2087–2102.

    Google Scholar 

  • Zhang, Q., Xu, L., Jia, J. Y., 2014, 100+ times faster weighted median filter (WMF): IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2830–2837.

    Google Scholar 

  • Zheng, J. L., Ying, Q. H., and Yang, W. L., 2011, Signals and systems(Third Edtion): Higher Education Press, China.

    Google Scholar 

Download references

Acknowledgements

Thanks to all the staff involved in the field data collection. Thanks to the editorial teachers and the anonymous peer review experts for valuable advices, it make the quality of the article greatly improved.

Author information

Authors and Affiliations

  1. School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China

    bi-ming Zhang, shi-kun Dai, qi-yun Jiang & guang Li

  2. Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha, 410083, China

    bi-ming Zhang, shi-kun Dai, qi-yun Jiang & guang Li

  3. School of Traffic and Transportation Engineering, Central South University, Changsha, 410075, China

    jian Yan

Authors
  1. bi-ming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. shi-kun Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. qi-yun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. jian Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. guang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to shi-kun Dai.

Additional information

This work is supported by the National Natural Science Foundation of China (No. 41574127 and No. 41227803).

Zhang Bi-Ming, student pursuing a PhD degree at CSU(Central South University), obtained his M.Sc. from CSU in 2006, his main research interests are data processing, data analysis, data visualization and parallel computing in electromagnetic exploration.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, bm., Dai, sk., Jiang, qy. et al. The study of time domain denoising for the time-frequency electromagnetic method prospecting data. Appl. Geophys. (2019). https://doi.org/10.1007/s11770-019-0788-8

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: https://doi.org/10.1007/s11770-019-0788-8

Keywords

Search

Navigation