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Deconvolution of teleseismicp-waves ...

Dasgupta, Saptarshi.

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Deconvolution of teleseismicp-waves using the SVA and autoregressive techniques.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deconvolution of teleseismicp-waves using the SVA and autoregressive techniques./
作者:	Dasgupta, Saptarshi.
面頁冊數:	137 p.
附註:	Adviser: Robert L. Nowack.
Contained By:	Dissertation Abstracts International68-12B.
標題:	Geophysics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3291066
ISBN:	9780549357506

Deconvolution of teleseismicp-waves using the SVA and autoregressive techniques.
Dasgupta, Saptarshi.

Deconvolution of teleseismicp-waves using the SVA and autoregressive techniques. - 137 p.

Adviser: Robert L. Nowack.

Thesis (Ph.D.)--Purdue University, 2007.

In this study, the deconvolution of three-component teleseismic P-waves is investigated using the autocorrelation of the P to SV scattered waves. By assuming that the P to SV scattering coefficients are random and white, the autocorrelation of the SV component (SVA) provides an estimate of the autocorrelation of the source and distant earth signature. This is similar to using the autocorrelation of a reflection seismogram for deconvolution in exploration seismology where the P to P scattering coefficients are assumed to be random and white. For earthquake data the source signature is generally not minimum phase. However a minimum phase wavelet obtained from the SV autocorrelation can be used to deconvolve the original data that have been processed to be minimum phase. The SVA approach has been tested using synthetic data and then applied to observed teleseismic data from the 1993 Cascadia experiment. Since deconvolution results are often restricted in frequency range, an extrapolation of the deconvolved frequency spectra is next investigated using an autoregressive (AR) approach and an extended time-domain deconvolution approach to obtain better temporal resolution. A prediction error filter is used to perform the autoregressive extrapolation to estimate the unknown spectral values of the deconvolution results. An extended time-domain deconvolution approach is also developed where the deconvolved spectra are enhanced using increased high-cut filters for the time-domain deconvolution. The AR and extended time-domain deconvolution approaches are compared using synthetic data and observed data from the GBA seismic array in India. The deconvolution of vertical component seismic data using the SVA technique is finally applied to selected INDEPTH II and CDSN seismic stations to investigate the P-wave velocity structure in southern Tibet. The deconvolved vertical component seismic data are inverted for P-velocity crustal structure and the resulting Moho depths at each station are compared with the depths obtained by the inversion of receiver functions by Mitra et al. (2005).

ISBN: 9780549357506Subjects--Topical Terms:

535228
Geophysics.

Deconvolution of teleseismicp-waves using the SVA and autoregressive techniques.
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502 $a Thesis (Ph.D.)--Purdue University, 2007.
520 $a In this study, the deconvolution of three-component teleseismic P-waves is investigated using the autocorrelation of the P to SV scattered waves. By assuming that the P to SV scattering coefficients are random and white, the autocorrelation of the SV component (SVA) provides an estimate of the autocorrelation of the source and distant earth signature. This is similar to using the autocorrelation of a reflection seismogram for deconvolution in exploration seismology where the P to P scattering coefficients are assumed to be random and white. For earthquake data the source signature is generally not minimum phase. However a minimum phase wavelet obtained from the SV autocorrelation can be used to deconvolve the original data that have been processed to be minimum phase. The SVA approach has been tested using synthetic data and then applied to observed teleseismic data from the 1993 Cascadia experiment. Since deconvolution results are often restricted in frequency range, an extrapolation of the deconvolved frequency spectra is next investigated using an autoregressive (AR) approach and an extended time-domain deconvolution approach to obtain better temporal resolution. A prediction error filter is used to perform the autoregressive extrapolation to estimate the unknown spectral values of the deconvolution results. An extended time-domain deconvolution approach is also developed where the deconvolved spectra are enhanced using increased high-cut filters for the time-domain deconvolution. The AR and extended time-domain deconvolution approaches are compared using synthetic data and observed data from the GBA seismic array in India. The deconvolution of vertical component seismic data using the SVA technique is finally applied to selected INDEPTH II and CDSN seismic stations to investigate the P-wave velocity structure in southern Tibet. The deconvolved vertical component seismic data are inverted for P-velocity crustal structure and the resulting Moho depths at each station are compared with the depths obtained by the inversion of receiver functions by Mitra et al. (2005).
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