Grey wolf optimization-based variational mode decomposition for magnetotelluric data combined with detrended fluctuation analysis

• Autorzy: Zhang Xian , Li Diquan , Li Jin , Li Yong

Identyfikatory

DOI

10.1007/s11600-021-00714-2

• Języki publikacji: EN

Abstrakty

EN

We present a magnetotelluric data denoising method that uses grey wolf optimization to optimize variational mode decomposition and combines it with detrended fluctuation analysis. First, envelope entropy is selected as the fitness function for grey wolf optimization and is used to determine the number of modes K and the penalty factor, which are the key parameters of the variational mode decomposition method. Then, the optimized variational mode decomposition method is used to decompose magnetotelluric data. Finally, the scaling exponent in detrended fluctuation analysis is used to determine the corresponding intrinsic mode function components to superimpose and reconstruct the useful magnetotelluric data. Extensive experiments and thorough analyses are performed on the synthetic data and field data. The results of the proposed method are compared with the results of the remote reference, variational mode decomposition, variational mode decomposition and matching pursuit, variational mode decomposition and detrended fluctuation analysis methods; the proposed method can improve the denoising performance and reliability of low-frequency magnetotelluric data. The reconstructed data are closer to the natural magnetotelluric data. The satisfactory performance in the results verifies the effectiveness of the design and optimization method.

Słowa kluczowe

EN

magnetotellurics; denoising; variational mode decomposition; Grey Wolf Optimization

PL

magnetotelluryka; odszumianie; dekompozycja w trybie wariacyjnym

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 1
• Strony: 111--120
• Opis fizyczny: Bibliogr. 38 poz.

Twórcy

autor

Zhang Xian

• School of Geosciences and Info-physics, Central South University, Changsha, China

autor

Li Diquan

• School of Geosciences and Info-physics, Central South University, Changsha, China

autor

Li Jin

• College of Information Science and Engineering, Hunan Normal University, Changsha, China
• Key Laboratory of Geophysical Electromagnetic Probing Technologies of Ministry of Natural Resources, Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Science, Langfang, China

• https://orcid.org/0000-0001-6606-6967

autor

Li Yong

• Key Laboratory of Geophysical Electromagnetic Probing Technologies of Ministry of Natural Resources, Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Science, Langfang, China

Bibliografia

• 1. Agarwal A, Chandra A, Shalivahan S, Singh RK (2017) Grey wolf optimizer: a new strategy to invert geophysical data sets. Geophys Prospect 66(6):1215–1226
• 2. Boudraa AO, Cexus JC (2007) EMD-based Signal Filtering. IEEE T Instrum Meas 56(6):2196–2202
• 3. Cagniard L (1953) Basic theory of the magneto-telluric method of geophysical prospecting. Geophysics 18(3):605–635
• 4. Cai JH, Chen QY (2015) Spectrum analysis of magnetotelluric data series based on EMD-teager transform. Pure Appl Geophys 172(10):2901–2915
• 5. Cai JH, Tang JT, Hua XR, Gong YR (2009) An analysis method for magnetotelluric data based on the Hilbert-Huang transform. Explor Geophys 40(2):197–205
• 6. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544
• 7. Egbert GD, Booker JR (1986) Robust estimation of geomagnetic transfer functions. Geophys J Int 87(1):173–194
• 8. Egbert GD, Livelybrooks DW (2001) Single station magnetotelluric transfer function estimates and the reliability of their variances. Geophys J Int 144(1):65–82
• 9. Gamble TD, Goubau WM, Clarke J (1979a) Magnetotelluric with a remote magnetic reference. Geophysics 44(1):53–68
• 10. Gamble TD, Goubau WM, Clarke J (1979b) Error analysis for remote magnetotelluric. Geophysics 44:959–968
• 11. Hermance JF (1973) Processing of magnetotelluric data. Phys Earth Planet 7(3):349–364
• 12. Huang NE, Shen Z, Long SR, Wu MC, Shih H, Zheng Q, Yen NC, Tung C, Liu H (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. P Roy Soc A-Math Phy 454(1971):903–995
• 13. Kohli M, Arora S (2017) Chaotic grey wolf optimization algorithm for constrained optimization problem. J Comput Des Eng 5(4):458–472
• 14. Kumar A, Zhou YQ, Xiang JW (2021) Optimization of VMD using kernel-based mutual information for the extraction of weak features to detect bearing. Measurement 168:108402
• 15. Leung-Yan-Cheong S, Cover T (1978) Some equivalences between shannon entropy and Kolmogorov complexity. IEEE T Inform Theory 24(3):331–338
• 16. Li ZP, Chen JL, Zi YY, Pan J (2017) Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive. Mech Syst Signal Pr 85(2):512–529
• 17. Li J, Zhang X, Cai J (2019) Suppression of strong interference for AMT using VMD and MP. Chinese J Geophys 62(10):3866–3884
• 18. Li J, Liu XQ, Li G, Tang JT (2020a) Magnetotelluric noise suppression based on impulsive atoms and NPSO-OMP algorithm. Pure Appl Geophys 177(11):5275–5297
• 19. Li J, Zhang X, Tang JT (2020b) Noise suppression for magnetotelluric using variational mode decomposition and detrended fluctuation analysis. J Appl Geophys 180:104127
• 20. Li J, Peng YQ, Tang JT, Li Y (2021) Denoising of magnetotelluric data using K-SVD dictionary training. Geophys Prospect 69(2):448–473
• 21. Ling ZB, Wang PY, Wan YX, Li TL (2019) Effective denoising of magnetotelluric (MT) data using a combined wavelet method. Acta Geophys 67:813–824
• 22. Liu YY, Yang GL, Li M, Yin HL (2016) Variational mode decomposition denoising combined the detrended fluctuation analysis. Signal Process 125(8):349–364
• 23. Mert A, Akan A (2014) Detrended fluctuation thresholding for empirical mode decomposition based de-noising. Digit Signal Process 32(9):48–65
• 24. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer Adv Eng Softw 69:46–61
• 25. Neukirch M, Garcia X (2014) Nonstationary magnetotelluric data processing with instantaneous parameter. J Geophys Res Solid Earth 119(3):1634–1654
• 26. Rosenbrock HH (1960) An automatic method for finding the greatest or least value of a function. Comput J 3(3):175–184
• 27. Sivavaraprasad G, Sree Padmaja R, Venkata Ratnam D (2017) Mitigation of ionospheric scintillation effects on GNSS signals using variational mode decomposition. IEEE Geosci Remote Sens Lett 14(4):389–392
• 28. Song EZ, Ke Y, Yao C, Dong Q, Yang LP (2019) Fault diagnosis method for high-pressure common rail injector based on IFOA-VMD and hierarchical dispersion entropy. Entropy 21(10):923
• 29. Tang JT, Li J, Xiao X, Zhang LC, Lv QT (2012) Mathematical morphology filtering and noise suppression of magnetotelluric sounding data. Chinese J Geophys 55(5):1784–1793
• 30. Trad DO, Travassos JM (2000) Wavelet filtering of magnetotelluric data. Geophysics 65(2):482–491
• 31. Wang YX, Markert R (2016) Filter bank property of variational mode decomposition and its applications. Signal Process 120(3):509–521
• 32. Wang HD, Deng SE, Yang JX, Liao H, Li WB (2020a) Parameter-adaptive VMD method based on BAS optimization algorithm for incipient bearing fault diagnosis. Math Probl Eng 2020:5659618
• 33. Wang S, Li Y, Zhao Y (2020b) Desert seismic noise suppression based on multimodal residual convolutional neural network. Acta Geophys 68:389–401
• 34. Weckmann U, Magunia A, Ritter O (2005) Effective noise separation for magnetotelluric single site data processing using a frequency domain selection scheme. Geophys J Int 161(3):635–652
• 35. Xu Z, Qin C, Tang G (2021) A novel deconvolution cascaded variational mode decomposition for weak bearing fault detection with unknown signal transmission path. IEEE Sens J 21(2):1746–1755
• 36. Yi C, Lv Y, Dang Z (2016) A fault diagnosis scheme for rolling bearing based on particle swarm optimization in variational mode decomposition. Shock Vib 2016:9372691
• 37. Zhang X, Li J, Li DQ, Li Y, Liu B, Hu YF (2021) Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit. Earth Planets Space 73(1):76
• 38. Zhou C, Tang JT, Ren ZY, Xiao X, Tan J, Wu MA (2015) Application of Rhoplus method to audio magnetotelluric dead band distortion data. Chinese J Geophys 58(12):4648–4660

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).