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Methodology for dense spatial sampling of multicomponent recording of converted waves in shallow marine environments
Author(s) -
Nihed El Allouche,
Guy Drijkoningen,
Joost van der Neut
Publication year - 2010
Publication title -
geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.178
H-Index - 172
eISSN - 1942-2156
pISSN - 0016-8033
DOI - 10.1190/1.3481362
Subject(s) - computer science , aliasing , sampling (signal processing) , underwater , spatial filter , remote sensing , acoustics , geology , artificial intelligence , computer vision , filter (signal processing) , physics , oceanography
A widespread use of converted waves for shallow marine applications is hampered by spatial aliasing and field efficiency. Their short wavelengths require dense spatial sampling which often needs to be achieved by receivers deployed on the seabed. We adopted a new methodology where the dense spatial sampling is achieved in the common-receiver domain by reducing the shot spacing. This is done by shooting one track multiple times and merging the shot lines in an effective manner in a separate processing step. This processing step is essential because positioning errors introduced during the field measurement can become significant in the combined line, particularly when they exceed the distance between two adjacent shot positions. For this processing step, a particular shot line is used as a reference line and relative variations in source and receiver positions in the other shot lines are corrected for using crosscorrelation. The combined shot line can subsequently be regularized for further processing. The methodology is adopted in a field experiment conducted in the Danube River in Hungary. The aim of the seismic experiment was to acquire properly sampled converted-wave data using a multicomponent receiver array. The dense spatial sampling was achieved by sailing one track 14 times. After correcting for the underwater receiver positions using the direct arrival, the crosscorrelation step was applied to merge the different shot lines. The successfully combined result is regularized into a densely sampled data set free of visible spatial aliasing and suitable for converted-wave processing

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