A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Ensemble empirical mode decomposition: a noise-assisted data analysis method

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An Introduction To The Event Related Potential Technique

Interpretation theory in applied geophysics: Grant, F S ... Interpretation theory in applied geophysics Unknown Binding – January 1, 1965 5.0 out of 5 stars 1 rating. See all formats and editions Hide other formats and editions. The Amazon Book Review Book recommendations, author interviews, editors' picks, and more. Read it now. Enter your mobile number or email address below and we'll send you a ...

Interpretation Theory in Applied Geophysics Grant (McGraw-Hill, 140s.)

The GPR (Ground Penetrating Radar) conference in Hong Kong year 2016 marked the 30th anniversary of the initial meeting in Tifton, Georgia, USA on 1986. The conference has been being a bi-annual event and has been hosted by sixteen cities from four continents. Throughout these 30 years, researchers and practitioners witnessed the analog paper printout to digital era that enables very efficient collection, processing and 3D imaging of large amount of data required in GPR imaging in infrastructure. GPR has systematically progressed forward from “Locating and Testing” to “Imaging and Diagnosis” with the Holy Grail of ’Seeing the unseen’ becoming a reality. This paper reviews the latest development of the GPR’s primary infrastructure applications, namely buildings, pavements, bridges, tunnel liners, geotechnical and buried utilities. We review both the ability to assess structure as built character and the ability to indicate the state of deterioration. Finally, we outline the path to a more rigorous development in terms of standardization, accreditation, and procurement policy.

A review of Ground Penetrating Radar application in civil engineering: A 30-year journey from Locating and Testing to Imaging and Diagnosis

Time-frequency analysis plays a significant role in seismic data processing and interpretation. Complete ensemble empirical mode decomposition decomposes a seismic signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. Analysis on synthetic and real data demonstrates that this method promises higher spectral-spatial resolution than the short-time Fourier transform or wavelet transform. Application on field data thus offers the potential of highlighting subtle geologic structures that might otherwise escape unnoticed.

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Empirical mode decomposition for seismic time-frequency analysis

Geologic noise and background electromagnetic EM waves often degrade the quality of very low frequency electromagneticVLF-EMdata.Toretrievesignalswithsignificant geologic information, we used a new nonlinear decomposition technique called the empirical mode decomposition EMD method with the Hilbert transform. We conducted a 2Dresistivitymodelstudythatincludedinversionofthesyntheticdatatotesttheaccuracyandcapabilitiesofthismethod. Next, we applied this method to real data obtained from a fieldexperimentandageologicexample.ThefilteringprocedureforrealdatastartswithapplyingtheEMDmethodtodecompose the VLF data into a series of intrinsic mode functions that admit a well-behaved Hilbert transform. With the Hilbert transform, the intrinsic mode functions yielded a spectrogram that presents an energy-wavenumber-distance distribution of the VLF data. We then examined the decomposeddataandtheirspectrogramtodeterminethenoisecomponents, which we eliminated to obtain more reliable VLF data. The EMD-filtered data and their associated spectrograms indicated the successful application of this method. Because VLF data are recorded as a complex function of the real variable distance, the in-phase and quadrature parts are complementarycomponentsofeachotherandcouldbeaHilbert transform pair if the data are analytical and noise free. Therefore,bycomparingtheoriginaldatasetwiththeoneobtained from the Hilbert transform, we could evaluate data quality and could even replace the original with its Hilbert transform counterpart with acceptable accuracy. By application of both this technique and conventional methods to real data in this study, we have shown the superiority of this new method and have obtained a more reliable earth model by invertingtheEMD-filtereddata.

Chih Sung Chen

Papers

Noise reduction and data recovery for a VLF-EM survey using a nonlinear decomposition method

Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data

Subsurface GPR imaging of a potential collapse area in urban environments

Nonlinear data processing method for the signal enhancement of GPR data

Application of sub-image multiresolution analysis of Ground-penetrating radar data in a study of shallow structures