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Reprocessing of deep seismic reflection data from the North German Basin with the Common Reflection Surface stack

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Title: Reprocessing of deep seismic reflection data from the North German Basin with the Common Reflection Surface stack
Authors: Yoon, Mi-Kyung mi-kyung.yoon@zmaw.de, Baykulov, Mikhail, Dümmong, Stefan, Brink, Heinz-Jürgen, Gajewski, Dirk
Source: Tectonophysics. Jul2009, Vol. 472 Issue 1-4, p273-283. 11p.
Subjects: Seismic reflection method data processing, Geological basins, Surfaces (Technology), Seismic wave velocity, Signal-to-noise ratio, Sedimentary basins, Image processing
Abstract: Abstract: The Common Reflection Surface (CRS) stack is a promising alternative stacking technique for reflection data. This technique is an automatic multi-parameter stack that does not require the explicit knowledge of a stacking velocity model. Furthermore, it significantly improves the signal-to-noise ratio as it considers more traces during the zero-offset stacking than the conventional CMP stack. Especially, for low-fold data in heterogeneous settings, the CRS stack is able to further enhance reflections. Recently, the CRS stack was applied to reflection data from sedimentary basins for oil and gas exploration purposes. It was not yet applied to crustal and deeper reflection data. We used the CRS stack for deep reflection data from the North German Basin. The data were acquired in the late 1970''s and early 1980''s with recording times down to 15 s TWT and mean CMP-folds of about 20. We reprocessed the data focusing on lower crustal and deeper structures. Compared to the conventional CMP processing of the 1980''s, the reprocessed sections show an improved image quality at all time levels, i.e. from the sedimentary cover down to the crust and upper mantle. Especially, the reflections from the salt structures and Moho events are enhanced. Further, a constrained CRS stack was applied to the data using a stacking velocity model from conventional velocity analysis. The latter further improved the coherency and visibility of the reflections, especially in the area of the salt plugs and in the upper crust where multiples were suppressed. The data examples demonstrate that the CRS stack is a suitable tool for improving the quality of seismic images, especially for deep reflection data from low low-fold data acquisitions. [Copyright &y& Elsevier]
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Database: Engineering Source
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Abstract:Abstract: The Common Reflection Surface (CRS) stack is a promising alternative stacking technique for reflection data. This technique is an automatic multi-parameter stack that does not require the explicit knowledge of a stacking velocity model. Furthermore, it significantly improves the signal-to-noise ratio as it considers more traces during the zero-offset stacking than the conventional CMP stack. Especially, for low-fold data in heterogeneous settings, the CRS stack is able to further enhance reflections. Recently, the CRS stack was applied to reflection data from sedimentary basins for oil and gas exploration purposes. It was not yet applied to crustal and deeper reflection data. We used the CRS stack for deep reflection data from the North German Basin. The data were acquired in the late 1970''s and early 1980''s with recording times down to 15 s TWT and mean CMP-folds of about 20. We reprocessed the data focusing on lower crustal and deeper structures. Compared to the conventional CMP processing of the 1980''s, the reprocessed sections show an improved image quality at all time levels, i.e. from the sedimentary cover down to the crust and upper mantle. Especially, the reflections from the salt structures and Moho events are enhanced. Further, a constrained CRS stack was applied to the data using a stacking velocity model from conventional velocity analysis. The latter further improved the coherency and visibility of the reflections, especially in the area of the salt plugs and in the upper crust where multiples were suppressed. The data examples demonstrate that the CRS stack is a suitable tool for improving the quality of seismic images, especially for deep reflection data from low low-fold data acquisitions. [Copyright &y& Elsevier]
ISSN:00401951
DOI:10.1016/j.tecto.2008.05.010