Signatures of the Seismic Source in EMD-Based Characterization of the 1994 Northridge, California, Earthquake Recordings

Zhang, Ray Ruichong; Ma, Shuo; Hartzell, Stephen H.

doi:10.1785/0120010285

Cited by 68 publications

(21 citation statements)

References 30 publications

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“…However, empirically, the EMD has been shown to be effective in extracting relevant components in a variety of applications involving non-stationary signals. Its effectiveness has been demonstrated for processing audio signals [16], global position systems [17], gravitational waves [18], seismic signals [19], etc. While wavelet decomposition decomposes a signal into components using predefined filter banks, the EMD algorithm decomposes it into components whose modes of oscillations are adaptively decided by the nature of the signal.…”

Section: Sifting Processmentioning

confidence: 99%

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Narayanan¹,

Shastry²,

Chen³

et al. 2010

PIER B

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Abstract-In homeland security and law enforcement situations, it is often required to remotely detect human targets obscured by walls and barriers. In particular, we are specifically interested in scenarios that involve a human whose torso is stationary. We propose a technique to detect and characterize activity associated with a stationary human in through-the-wall scenarios using a Doppler radar system. The presence of stationary humans is identified by detecting Doppler signatures resulting from breathing, and movement of the human arm and wrist. The irregular, transient, non-uniform, and non-stationary nature of human activity presents a number of challenges in extracting and classifying Doppler signatures from the signal. These are addressed using bio-mechanical human arm movement models and the empirical mode decomposition (EMD) algorithm for Doppler feature extraction. Experimental results demonstrate the effectiveness of our approach to extract Doppler signatures corresponding to human activity through walls using a 750-MHz Doppler radar system.

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Section: Sifting Processmentioning

confidence: 99%

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Narayanan¹,

Shastry²,

Chen³

et al. 2010

PIER B

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“…The technique has been used extensively in the analysis of ocean wave data (Hwang et al 1999;Huang et al 2003) as well as in the analysis of polar ice cover . EMD has also been applied in the analysis of seismological data by Zhang et al (2003) and has even been used to diagnose heart rate fluctuations (Balocchi et al 2004).…”

Section: Emd Background and The Problemmentioning

confidence: 99%

Empirical mode decomposition using rational splines: an application to rainfall time series

2008

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Empirical mode decomposition ( EMD), a relatively new form of time-series decomposition, has the feature of not assuming that a time series is linear or stationary, as is implicitly done in Fourier analysis. In natural time series such as records of rainfall, streamflow, temperature, etc., where most variables exhibit nonlinear and nonstationary behaviour, this feature is particularly useful, allowing more meaningful quantification of the proportion of variance in a time series due to fluctuations at different time scales, than previous spectral analysis techniques. However, in its original form, the EMD algorithm relies on cubic spline interpolation of the extrema, which often inflate the variance of the resultant components (intrinsic mode functions) and residual. In this paper, a suggested improvement to the EMD algorithm, using rational splines and flexible treatment of the end conditions, is outlined and the consequent effect on three exemplary annual rainfall time series is assessed as a proof of concept. It is recognized that many more examples are needed for the fine tuning of the ideas before it can be widely recommended and this work is currently underway.

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“…The technique has been used extensively in the analysis of ocean wave data Hwang et al, 2003) as well as in the analysis of polar ice cover (Gloersen and Huang, 2003). EMD has also been applied in the analysis of seismological data by Zhang et al (2003) and has even been used to diagnose heart rate fluctuations (Balocchi et al, 2004). The algorithm for 1-D EMD is readily available in the cited literature, so is not repeated here; the 2-D extension is based on the 1-D algorithm and is elaborated in Sect.…”

Section: The Basic Principles Of the Emd In One Dimensionmentioning

confidence: 99%

Empirical Mode Decomposition on the sphere: application to the spatial scales of surface temperature variations

Fauchereau

Pegram

Sinclair

2008

Hydrol. Earth Syst. Sci.

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Abstract. Empirical Mode Decomposition (EMD) is applied here in two dimensions over the sphere to demonstrate its potential as a data-adaptive method of separating the different scales of spatial variability in a geophysical (climatological/meteorological) field. After a brief description of the basics of the EMD in 1 then 2 dimensions, the principles of its application on the sphere are explained, in particular via the use of a zonal equal area partitioning. EMD is first applied to an artificial dataset, demonstrating its capability in extracting the different (known) scales embedded in the field. The decomposition is then applied to a global mean surface temperature dataset, and we show qualitatively that it extracts successively larger scales of temperature variations related, for example, to topographic and large-scale, solar radiation forcing. We propose that EMD can be used as a global dataadaptive filter, which will be useful in analysing geophysical phenomena that arise as the result of forcings at multiple spatial scales.

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Signatures of the Seismic Source in EMD-Based Characterization of the 1994 Northridge, California, Earthquake Recordings

Cited by 68 publications

References 30 publications

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Empirical mode decomposition using rational splines: an application to rainfall time series

Empirical Mode Decomposition on the sphere: application to the spatial scales of surface temperature variations

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