Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering
Li Guang1, Xiao Xiao1, Tang Jing-Tian1, Li Jin1,2, Zhu Hui-Jie3, Zhou Cong1, and Yan Fa-Bao4
1. Institute of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
2. Institute of Physics and Information Science, Hunan Normal University, Changsha 410081, China.
3. The First Engineering Scientific Research Institute of General Armaments Department, Wuxi 214035, China.
4. Institute of Space Science, Shandong University, Weihai 264209, China.
Abstract：
In deep mineral exploration, the acquisition of audio magnetotelluric (AMT) data is severely affected by ambient noise near the observation sites; This near-field noise restricts investigation depths. Mathematical morphological filtering (MMF) proved effective in suppressing large-scale strong and variably shaped noise, typically low-frequency noise, but can not deal with pulse noise of AMT data. We combine compressive sensing and MMF. First, we use MMF to suppress the large-scale strong ambient noise; second, we use the improved orthogonal match pursuit (IOMP) algorithm to remove the residual pulse noise. To remove the noise and protect the useful AMT signal, a redundant dictionary that matches with spikes and is insensitive to the useful signal is designed. Synthetic and field data from the Luzong field suggest that the proposed method suppresses the near-source noise and preserves the signal well; thus, better results are obtained that improve the output of either MMF or IOMP.
. Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering[J]. APPLIED GEOPHYSICS, 2017, 14(4): 581-590.
[1]
Bollig, A., Disch, C., Arts, M., et al., 2017, SNR walls in eigenvalue-based spectrum sensing: Eurasip Journal on Wireless Communications & Networking, 2017(1), 109.
[2]
Cai, J. H., Tang, J. T., Hua, X. R., et al., 2009, An analysis method for magnetotelluric data based on the Hilbert-Huang Transform: Exploration Geophysics, 40(2), 197−205.
[3]
Candès, E. J., Romberg, J., and Tao, T., 2006, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information: IEEE Transactions on Information Theory, 52(2), 489−509.
[4]
Candès, E. J., and Tao, T., 2006, Near-optimal signal recovery from random projections: Universal encoding strategies: IEEE transactions on information theory, 52(12), 5406−5425.
[5]
Candès, E. J., and Wakin, M. B., 2008, An introduction to compressive sampling: Signal Processing Magazine, IEEE, 25(2), 21−30.
[6]
Dai, W., and Milenkovic, O., 2009, Subspace pursuit for compressive sensing signal reconstruction: IEEE Transactions on Information Theory, 55(5), 2230−42249.
[7]
Davenport, M. A., and Wakin, M. B., 2010, Analysis of orthogonal matching pursuit using the restricted isometry property: IEEE Transactions on Information Theory, 56(9), 4395−4401.
[8]
Donoho, D. L., 2006, Compressed sensing: IEEE Transactions on information theory, 52(4), 1289−1306.
[9]
Duarte, M. F., Davenport, M. A., Takhar, D., et al., 2008, Single-pixel imaging via compressive sampling: IEEE Signal Processing Magazine, 25(2), 83−91.
[10]
Egbert, G. D., 1997, Robust multiple-station magnetotelluric data processing: Geophysical Journal International, 130(2), 475−496.
[11]
Gamble, T. D., Goubau, W. M., and Clarke, J., 1979, Magnetotellurics with a remote magnetic reference: Geophysics, 44(1), 53−68.
[12]
Garcia, X., and Jones, A. G., 2008, Robust processing of magnetotelluric data in the AMT dead band using the continuous wavelet transform: Geophysics, 73(6), F223−F234.
[13]
Guo, H. Y., Yang, Z., and Zhu, W. P., 2012, Anewsingle-channel speech separation method based on sparse decomposition: Acta Electronica Sinica, 40(4), 762−768.
[14]
Hennenfent, G., and Herrmann, F. J., 2008, Simply denoise: Wavefield reconstruction via jittered undersampling: Geophysics, 73(3), V19−V28.
[15]
Huang, H., and Makur, A., 2011, Backtracking-based matching pursuit method for sparse signal reconstruction: IEEE Signal Processing Letters, 18(7), 391−394.
[16]
Jin, J., Gu, Y. T., and Mei, S. L., 2010, An Introduction to Compressive Sampling and Its Applications: Journal of Electronics & Information Technology, 32(2), 470−475.
[17]
Li, F. L., Chang, J., Zhang, X. Y., et al., 2015, Reconstruction algorithm of blind sparse and its de-noising application in speech based on compressed sensing: Journal of Central South University, 46(1), 164−170.
[18]
Li, G., Tang, J. T., Wen, S. S., et al., 2017, Suppression of strong noise in MT data based on reconstruction algorithm of compressive sensing: International Geophysical Conference, 17−20 April, Qingdao, China, 497−499.
[19]
Li, J., Tang, J. T., Wang, L., et al., 2014, Noise suppression for magnetotelluric sounding data based on signal subspace enhancement and endpoint detection: Acta Physica Sinica, 63(1), 247−254.
[20]
Li, Q., Han, L. G., Chen, J. Y., et al., 2015, Separation of inhomogeneous blended seismic data: Applied Geophysics, 12(3), 327−333.
[21]
Li, S. T., and Wei, D., 2009, A Survey on Compressive Sensing: Acta Automatica Sinica, 35(11), 1369−1377.
[22]
Liu,Y. X., Zhao, R. Z., Hu, S. H., et al., 2010, Regularized Adaptive Matching Pursuit Algorithm for Signal Reconstruction Basedon Compressive Sensing: Journal of Electronics & Information Technology, 32(11), 2713−2717.
[23]
Matheron, G., and Serra, J., 2000, The Birth of Mathematical Morphology: Proceedings of the 5th International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing, ISMM 2000, Palo Alto, CA, USA, June 26−28.
[24]
Mallat, S. G., and Zhang, Z., 1993, Matching pursuits with time-frequency dictionaries: IEEE Transactions on signal Processing, 41(12), 3397−3415.
[25]
Needell, D., and Tropp, J. A., 2009, CoSaMP: Iterative signal recovery from incomplete and inaccurate samples: Applied and Computational Harmonic Analysis, 26(3), 301−321.
[26]
Pati, Y. C., Rezaiifar, R., and Krishnaprasad, P. S., 1993, Orthogonal matching pursuits: Recursive function approximation with applications to wavelet decomposition: Proceedings of the 27th Asilomar Conference in Signals, Systems, and Computers.
[27]
Ren, Z., Kalscheuer, T., Greenhalgh, S., et al., 2013, A goal-oriented adaptive finite-element approach for plane wave 3-D electromagnetic modelling: Geophysical Journal International, 194(2), 700-718.
[28]
Shireesha, M., and Harinarayana, T., 2011, Processing of magnetotelluric data - a comparative study with 4 and 6 element impedance tensor elements: Applied Geophysics, 8(4), 285−292.
[29]
Shi, G. M., Liu, D. H., Gao, D. H., et al., 2009, Advances in theory and application of compressed sensing: Acta Electronica Sinica, 37(5), 10701−1081.
[30]
Smirnov, M. Y., 2003, Magnetotelluric data processing with a robust statistical procedure having a high breakdown point: Geophysical Journal International, 152(1), 1−7.
[31]
Tang, G., Ma, J. W., and Yang, H. Z., 2012a, Seismic data denoising based on learning-type overcomplete dictionaries: Applied Geophysics, 9(1), 27−32.
[32]
Tang, J. T., Hua, X. R., Cao, Z. M., et al., 2008, Hilbert-Huang transformation and noise suppression of magnetotelluric sounding data: Chinese J. Geophys (in Chinese), 51(2), 603−610.
[33]
Tang, J. T., Li, H., Li, J., et al., 2014, Top-Hat transformation and magnetotelluric sounding data strong interference separation of Lijiang-Zongyang ore concentration area: Journal of Jilin University (Earth Science Edition) (in Chinese), 44(1), 336−343.
[34]
Tang, J. T., Li, J., Xiao, X., et al., 2012b, Mathematical morphology filtering an noise suppression of magnetotelluric sounding data: Chinese J. Geophys (in Chinese), 55(05), 1784−1793.
[35]
Tang, J. T., Liu, Z. J., Liu, F. Y., et al., 2015, The denoising of the audio magnetotelluric data set with strong interferences: Chinese J. Geophys (in Chinese), 58(12), 4636−4647.
[36]
Tang, J. T., Xu, Z. M., Xiao, X., et al., 2012c, Effect rules of strong noise on magnetotelluric (MT) sounding in the Luzong ore cluster area: Chinese J. Geophys (in Chinese), 55(12), 4147−4159.
[37]
Tang, J. T., Zhou, C., Wang, X. Y., et al., 2013, Deep electrical structure and geological significance of Tongling ore district: Tectonophysics, 606, 78−96.
[38]
Trad, D. O., and Travassos, J. M., 2000, Wavelet filtering of magnetotelluric data: Geophysics, 65(2), 482−491.
[39]
Tropp, J. A., and Gilbert, A. C., 2007, Signal recovery from random measurements via orthogonal matching pursuit:IEEE Transactions on Information Theory, 53(12), 4655−4666.
[40]
Wang, X., Zhu, H., Rui, T., et al., 2015, Shift invariant sparse coding ensemble and its application in rolling bearing fault diagnosis: Journal of Vibroengineering, 17(4), 1837−1848.
[41]
Xiao, X., Tang, J. T., Zhou, C., et al., 2011, Magnetotelluric sounding in the Lujiang-Zongyang ore district and preliminary study of electrical structure: Acta Geological Sinica (in Chinese), 85(5), 873−886.
[42]
Zhang, H., Chen, X. H., and Zhang, L. Y., 2017, 3D simultaneous seismic data reconstruction and noise suppression based on the curvelet transform: Applied Geophysics, 14(1), 87−95.
[43]
Zhu, H. J., Wang, X. Q., Rui, T., et al., 2015, Implication of improved matching pursuit in de-noising for square wave: Journal of PLA University of Science and Technology (Natural Science Edition) (in Chinese), 16(4), 305−309.
2017, Vol. 14 
