Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis

2013 ◽  
Vol 30 (6) ◽  
pp. 74-86 ◽  
Author(s):  
Danilo P. Mandic ◽  
Naveed ur Rehman ◽  
Zhaohua Wu ◽  
Norden E. Huang
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen-yang Ma ◽  
Li Wu ◽  
Miao Sun ◽  
Qing Yuan

The traditional empirical mode decomposition method cannot accurately extract the time-frequency characteristic parameters contained in the noisy seismic monitoring signals. In this paper, the time-frequency analysis model of CEEMD-MPE-HT is established by introducing the multiscale permutation entropy (MPE), combining with the optimized empirical mode decomposition (CEEMD) and Hilbert transform (HT). The accuracy of the model is verified by the simulation signal mixed with noise. Based on the project of Loushan two-to-four in situ expansion tunnel, a CEEMD-MPE-HT model is used to extract and analyze the time-frequency characteristic parameters of blasting seismic signals. The results show that the energy of the seismic wave signal is mainly concentrated in the frequency band above 100 Hz, while the natural vibration frequency of the adjacent existing tunnel is far less than this frequency band, and the excavation blasting of the tunnel will not cause the resonance of the adjacent existing tunnel.


2014 ◽  
Vol 543-547 ◽  
pp. 2229-2233
Author(s):  
Hao Chen ◽  
Jun Hai Guo

The echoes of pulse radar from maneuvering targets are amplitude modulation and frequency modulation (AM-FM) signal. At present, the methods of estimating parameters of AM-FM signal are time-frequency analysis method, empirical mode decomposition and empirical wavelet transform based adaptive data analysis methods. This paper takes the idea of intrinsic mode function in guessing the initial phase, and applies the newly developed sparse time-frequency analysis method in AM-FM signal parameter estimation. Simulation results show that the estimating performance of this method in AM-FM signal is good under different SNR and it has low computational cost, and this method is applicable in target acceleration and velocity estimation.


Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.


2013 ◽  
Vol 5 (3/4) ◽  
pp. 231
Author(s):  
Azzedine Dliou ◽  
Rachid Latif ◽  
Mostafa Laaboubi ◽  
Fadel Mrabih Rabou Maoulainine ◽  
Samir Elouaham

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