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CivilComp Proceedings
ISSN 17593433 CCP: 83
PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: B.H.V. Topping, G. Montero and R. Montenegro
Paper 243
Lp Deconvolution of Seismic Data Using the Iterative ReWeighted Least Squares Method A.A. Chanerley^{1} and N.A. Alexander^{2}
^{1}School of Computing and Technology, University of East London, United Kingdom
A.A. Chanerley, N.A. Alexander, "Lp Deconvolution of Seismic Data Using the Iterative ReWeighted Least Squares Method", in B.H.V. Topping, G. Montero, R. Montenegro, (Editors), "Proceedings of the Eighth International Conference on Computational Structures Technology", CivilComp Press, Stirlingshire, UK, Paper 243, 2006. doi:10.4203/ccp.83.243
Keywords: correction, filter, seismic, wavelet, denoising, recursive, least squares, bandpass, filtering, inverse filter, convolve, deconvolve.
Summary
This method was used on synthetic and real seismic data [1,2] in order to determine
its sensitivity to bursts of noise. These can be of short duration but large amplitude,
which would usually be associated with unstable instrument operation, but can also
occur as a naturally occurring transient. In this case the iterative reweighted least squares (IRLS) method is applied as a general
deconvolution technique [3,4,5] for decoupling the instrument response from the
seismic data in order to obtain an estimate of the true ground motion.
The least squares approach uses optimisation, however the IRLS is an algorithm for determining optimisations where . The algorithm applies different weighting at each iteration of the algorithm. The optimisation problem may be stated as however at each iteration the weights are calculated from the residuals, , the difference between the desired and predicted. The seismic data used for the analysis is chosen as far as possible such that instrument parameters were available in the seismic record [6,7]. In particular this includes data from the SMART1 array in Taiwan which provide some details of the antialias filter used. For other digital records without details of any antialias filter, these can be inferred from the sampling rate, on the assumption that an antialias with a cutoff at half the sampling rate would have been used. The IRLS was applied to the seismic data after wavlet denoiseing and correcting for the baseline, but without any frequency selective filtering. In addition wavelet decomposition is used for further analysis of seismic data by showing separately more of the low and high frequency details of the data. References
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