An Approach to Seismic Correction which includes Wavelet De-noising

Chanerley, AA; Alexander, Nicholas A.

doi:10.4203/ccp.75.44

Cited by 9 publications

(11 citation statements)

References 6 publications

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“…However, its performance, in terms of computational cost and fidelity, is not as good as the Recursive Least Square (RLS) and Square Root RLS algorithms. The RLS algorithm [4,[8][9][10] was chosen for inverse system identification in preference to the LMS adaptive algorithm. Another reason is that the RLS algorithm is dependent on the incoming data samples rather than the statistics of the ensemble average as in the case of the LMS algorithm.…”

Section: Inverse Filtering Using Adaptive Algorithmsmentioning

confidence: 99%

“…A correction technique needs to (i) digitise, that is equisample the data, (ii) correct for instrument characteristics (iii) de-trend, (iv) de-noise with wavelets, or band-pass filter (v) resample to an appropriate sampling rate. A review of various proposed schemes is presented in [3,4]. The sequence of the component (ii) to (v) and exact algorithms used in these correction techniques vary significantly, as can the resulting recaptured ''original ground motion'' itself.…”

Section: Introductionmentioning

confidence: 99%

“…The authors did not want to present time series ground motion that have imposed and incorrect processing, but they present data without instrument correction which is not necessarily useful. This paper builds on the work [1,4] to discuss an implementation of the recursive least squares (RLS) algorithm in the context of a system identification problem. The RLS algorithm is used to determine, a posteriori, the filter characteristic or fingerprint, if you like, that the instrument leaves imposed on the time series.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising

Chanerley

Alexander

2007

Computers & Structures

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Section: Inverse Filtering Using Adaptive Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising

Chanerley

Alexander

2007

Computers & Structures

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“…Moreover the method need not be transform specific, though the choice of transform is guided by linear phase requirements. In some cases the wavelet transform can automatically correct for baseline shifts and extract the fling, in other cases a straightforward correction can be applied which relies only on improving the extracted long-period fling and pulse-like velocity and therefore removes some of the decisions in the published methods of Alexander (2008, 2007), Chen and Loh (2006), Wu and Wu (2007), Iwan et al (1985), Boore (2001), Alexander et al (2001) and Chanerley and Alexander (2002). It is a simplified procedure for baseline correction, which makes it easier to integrate to displacement.…”

mentioning

confidence: 98%

Obtaining estimates of the low-frequency ‘fling’, instrument tilts and displacement timeseries using wavelet decomposition

Chanerley

Alexander

2009

Bull Earthquake Eng

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This paper proposes a novel, wavelet-based algorithm, which by extracting the low-frequency fling makes it possible to automatically correct for baseline shift and re-integrate down to displacement. The algorithm applies a stationary-wavelet transform at a suitable level of decomposition to extract the low frequency fling model in the acceleration time histories. The low frequency, acceleration fling should be as close as possible to the theoretical type A model, which after correction leads to a pulse-type velocity and ramp-like displacement after first and second integration. The wavelet transform essentially decomposes the seismic record using maximally flat filters and these together with a de-noising scheme form the core of this approach, which is to extract the lower and higher frequency sub-band acceleration, velocity and displacement profiles and correct for baseline shift. The correction automatically selects one time point from the low-frequency sub-band and then zeros the acceleration baseline after the fling. This implies pure, translation without any instrument tilts. Estimates of instrument tilt angles are also obtainable from the wavelet transformed time history as well as estimates of signal-to-noise ratios. The acceleration data used in this study is from station TCU068 in the near-fault region of the Chi-Chi, Taiwan, earthquake of 20th September 1999.

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“…This decomposition could be a Fourier series, but in this paper we shall employ a wavelet decomposition using the stationary wavelet transform (SWT; Chanerley and Alexander, 2002Berrill et al, 2011;Chanerley et al, 2013). In expression (3), the total number of terms is m, that is m − 1 wavelet detail levels plus 1 wavelet approximation level.…”

Section: Application Of Volterra Seriesmentioning

confidence: 99%

Obtaining Spectrum Matching Time Series Using a Reweighted Volterra Series Algorithm (RVSA)

Alexander¹,

Chanerley²,

Crewe³

et al. 2014

Bulletin of the Seismological Society of America

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In this paper, we introduce a novel algorithm for morphing any accelerogram into a spectrum matching one. First, the seed time series is re-expressed as a discrete Volterra series. The first-order Volterra kernel is estimated by a multilevel wavelet decomposition using the stationary wavelet transform. Second, the higher-order Volterra kernels are estimated using a complete multinomial mixing of the first-order kernel functions. Finally, the weighting of every term in this Volterra series is optimally adapted using a Levenberg-Marquardt algorithm such that the modified time series matches any target response spectrum. Comparisons are made using the SeismoMatch algorithm, and this reweighted Volterra series algorithm is demonstrated to be considerably more robust, matching the target spectrum more faithfully. This is achieved while qualitatively maintaining the original signal's nonstationary statistics, such as general envelope, time location of large pulses, and variation of frequency content with time.

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An Approach to Seismic Correction which includes Wavelet De-noising

Cited by 9 publications

References 6 publications

Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising

Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising

Obtaining estimates of the low-frequency ‘fling’, instrument tilts and displacement timeseries using wavelet decomposition

Obtaining Spectrum Matching Time Series Using a Reweighted Volterra Series Algorithm (RVSA)

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