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Advanced signal processing tools for dispersive wavesNormal access

Authors: J.I. Mars, F. Glangeaud and J.L. Mari
Issue: Vol 2, No 4, November 2004 pp. 199 - 210
DOI: 10.3997/1873-0604.2004017
Special Topic: Seismic Surface Waves
Language: English
Info: Article, PDF ( 7.62Mb )

Two field examples are presented, showing the advantages of using multicomponent sensors for surface-wave studies. Multicomponent sensors allow the use of specific signal-processing tools such as the multicomponent singular value decomposition filter and the multicomponent polarization filter, which are both very efficient at separating surface waves from the other waves that comprise a seismic field record. Firstly, some signal-processing tools for studying surface waves are described. The various filters range from classical to advanced techniques. For processing single-component data, the filters are the f–k filter and filters based on singular value decomposition and on spectral matrix decomposition. For processing multicomponent data, the filters are the 4C-singular value decomposition filter and the classical or high-order polarization filter. Secondly, processing sequences that can be applied to the field data are described and the single-component processing sequence and the multicomponent processing sequence are compared. Two field examples are presented. The first data set is a land seismic data recording on 2C sensors. The second data set was obtained from a marine acquisition with OBS (4 components). The results obtained illustrate the advantages of using multicomponent filters. The efficiency of the 4C-SVD filter and the high-order statistic polarization filter is demonstrated.

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