An adaptive seismic signal denoising method based on variational mode decomposition

Abstract In this paper, we study the denoising method for unattended ground sensor (UGS) systems that are composed of seismic sensors. Seismic sensors are used for collecting the seismic signals generated by targets. The collected signals are polluted by noises during propagation, which will influence the performance of UGS systems. Thus, it is necessary to suppress the noises in the collected signals. This paper proposes a denoising method based on variational mode decomposition (VMD) and wavelet threshold denoising. This method decomposes raw seismic signal to several modes. Correlation coefficient and the Euclidean distance are combined to determine the cut-off points of the modes needed to be processed. Finally, the denoised signal is reconstructed. We evaluate the method on the basis of computer-synthesized and real-world seismic signals. The results show that the proposed method can effectively reduce noise components and retain effective components in raw seismic signals, which indicates a good performance.

[1]  Chi Zhang.  Motion Planning for Robotic Smooth Catching of Moving Object , 2018 .

[2]  Abdel-Ouahab Boudraa,et al.  EMD-Based Signal Filtering , 2007, IEEE Transactions on Instrumentation and Measurement.

[3]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[4]  Abdel-Ouahab Boudraa,et al.  Emd-based filtering using the Hausdorff distance , 2012, 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[5]  S. Poornachandra,et al.  Wavelet-based denoising using subband dependent threshold for ECG signals , 2008, Digit. Signal Process..

[6]  Subrat Kar,et al.  Detection of an Intruder and Prediction of His State of Motion by Using Seismic Sensor , 2018, IEEE Sensors Journal.

[7]  Harish Kumar Sardana,et al.  Estimation of direction of arrival of a moving target using subspace based approaches , 2016, SPIE Defense + Security.

[8]  Babak Hossein Khalaj,et al.  Grey Prediction Based Handoff Algorithm , 2007 .

[9]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  Steve McLaughlin,et al.  Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding , 2009, IEEE Transactions on Signal Processing.

[11]  Alex Pakhomov,et al.  Seismic signal and noise assessment for footstep detection range estimation in different environments , 2004, SPIE Defense + Commercial Sensing.

[12]  Guglielmo Lanzani,et al.  Laser-Inscribed Glass Microfluidic Device for Non-Mixing Flow of Miscible Solvents , 2018, Micromachines.

[13]  N. Amardjia,et al.  Α Modified EMD-ACWA Denoising Scheme using a Noise-only Model , 2020 .

[14]  Wenping Ma,et al.  Variational mode decomposition denoising combined with the Hausdorff distance. , 2017, The Review of scientific instruments.

[15]  Jingyi Lu.  Algorithm of Sound Signal Enhancement Based on VMD , 2018 .

[16]  Jiajun Han,et al.  Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding , 2015 .

[17]  Yue Li,et al.  Low-Frequency Noise Suppression of Desert Seismic Data Based on Variational Mode Decomposition and Low-Rank Component Extraction , 2020, IEEE Geoscience and Remote Sensing Letters.

[18]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[19]  Chao Zhang,et al.  Desert seismic signal denoising by 2D compact variational mode decomposition , 2019, Journal of Geophysics and Engineering.

[20]  Fang Li,et al.  Wavelet denoising and feature extraction of seismic signal for footstep detection , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[21]  Jun Liu,et al.  High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold , 2019, Micromachines.

[22]  J. Mohanalin,et al.  Wavelet based seismic signal de-noising using Shannon and Tsallis entropy , 2012, Comput. Math. Appl..

[23]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[24]  Andy W. H. Khong,et al.  Footstep detection and denoising using a single triaxial geophone , 2010, 2010 IEEE Asia Pacific Conference on Circuits and Systems.

[25]  Aydin Kizilkaya,et al.  Different Scenarios on Denoising of Signals in the Intrinsic Mode Function Selection Framework , 2016 .

[26]  Leonardo Zao,et al.  Speech Enhancement with EMD and Hurst-Based Mode Selection , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[27]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[28]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[29]  Abdel-Ouahab Boudraa,et al.  EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs , 2014, IEEE Transactions on Instrumentation and Measurement.

[30]  A. Boudraa,et al.  EMD-Based Signal Noise Reduction , 2005 .

[31]  Sean Schumer.  Analysis of human footsteps utilizing multi-axial seismic fusion , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[32]  Jianming Yu,et al.  Research on the seismic signal denoising with the LMD and EMD method , 2017, 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[33]  Xiulan Wen,et al.  Denoising of seismic signals based on empirical mode decomposition-wavelet thresholding: , 2020 .