Seismic depth imaging of sequence boundaries beneath the New Jersey shelf View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

M. Riedel, S. Reiche, K. Aßhoff, S. Buske

ABSTRACT

Numerical modelling of fluid flow and transport processes relies on a well-constrained geological model, which is usually provided by seismic reflection surveys. In the New Jersey shelf area a large number of 2D seismic profiles provide an extensive database for constructing a reliable geological model. However, for the purpose of modelling groundwater flow, the seismic data need to be depth-converted which is usually accomplished using complementary data from borehole logs. Due to the limited availability of such data in the New Jersey shelf, we propose a two-stage processing strategy with particular emphasis on reflection tomography and pre-stack depth imaging. We apply this workflow to a seismic section crossing the entire New Jersey shelf. Due to the tomography-based velocity modelling, the processing flow does not depend on the availability of borehole logging data. Nonetheless, we validate our results by comparing the migrated depths of selected geological horizons to borehole core data from the IODP expedition 313 drill sites, located at three positions along our seismic line. The comparison yields that in the top 450 m of the migrated section, most of the selected reflectors were positioned with an accuracy close to the seismic resolution limit (≈ 4 m) for that data. For deeper layers the accuracy still remains within one seismic wavelength for the majority of the tested horizons. These results demonstrate that the processed seismic data provide a reliable basis for constructing a hydrogeological model. Furthermore, the proposed workflow can be applied to other seismic profiles in the New Jersey shelf, which will lead to an even better constrained model. More... »

PAGES

17-32

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11001-018-9360-9

DOI

http://dx.doi.org/10.1007/s11001-018-9360-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104369163


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