New techniques in the analysis of geophysical data modelled as a multichannel autoregressive random process
Geophysical measurements can often be described in terms of multichannel, autoregressive data models from which one can directly derive measures of the harmonic composition of the underlying geophysical process and its inherent self-predictability. === We explore methods for and uses of multichannel...
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| Format: | Others |
| Language: | en |
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McGill University
1983
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| Online Access: | http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=71838 |
| Summary: | Geophysical measurements can often be described in terms of multichannel, autoregressive data models from which one can directly derive measures of the harmonic composition of the underlying geophysical process and its inherent self-predictability. === We explore methods for and uses of multichannel autoregressive data modelling in a geophysical context. === Autoregressive data modelling using the least-squares linear prediction method is generalized to multichannel time series. A recursive algorithm is obtained for the formation of the system of multichannel normal equations which determine the least-squares solution of the multichannel linear prediction problem. Solution of these multichannel normal equations is accomplished by the Cholesky factorization method. === The corresponding multichannel Maximum Entropy spectra derived from these least-squares estimates of the autoregressive model parameters are compared to that obtained using those parameters estimated by a multichannel generalization of Burg's algorithm. Numerical experiments have shown that the multichannel spectra obtained using the least-squares method provides for more accurate frequency determination for truncated sinusoids in the presence of additive white noise. |
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