A model reduction algorithm by spline approximation in the deconvolution of seismic signals

Kollias, S; Halkias, C

dc.contributor.author	Kollias, S	en
dc.contributor.author	Halkias, C	en
dc.date.accessioned	2014-03-01T02:47:33Z
dc.date.available	2014-03-01T02:47:33Z
dc.date.issued	1983	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33230
dc.subject	Adaptive Estimation	en
dc.subject	arma model	en
dc.subject	Cubic Spline	en
dc.subject	Model Reduction	en
dc.subject	piecewise linear	en
dc.subject	Reflection Coefficient	en
dc.subject	State Space Representation	en
dc.subject	kalman filter	en
dc.title	A model reduction algorithm by spline approximation in the deconvolution of seismic signals	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICASSP.1983.1171954	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICASSP.1983.1171954	en
heal.publicationDate	1983	en
heal.abstract	This paper presents a new method for obtaining ap - proximate low-order recursive (ARMA) realizations of convolution models. It is based upon optimum piecewise linear or cubic spline approximation of the convolution kernel. The method may be efficiently used in the deconvolution of seismic signal. The basic seismic wavelet is approximated by splines and a fixed-lag smoothing state-space representation, equivalent	en
heal.journalName	International Conference on Acoustics, Speech, and Signal Processing	en
dc.identifier.doi	10.1109/ICASSP.1983.1171954	en