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Velocity Tomography Imaging Method with Variable Grid spacing/Interval
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Geophysics.
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2013 (English)In: Oil Geophysical Prospecting, ISSN 1000-7210, Vol. 48, no 3, 379-389 p.Article in journal (Refereed) Published
Abstract [en]

In variable grid spacing tomography the underground velocity distribution is parameterized with model cells of different sizes. This method can simultaneously take into account the spatially varying resolution inherent in most datasets. E.g., due to experimental design or logistic constraints, the shallow and deep subsurface velocity distribution may be very differently determined by the available data. The variable grid spacing tomography and regular grid spacing tomography are similar in most other aspects. There are two main differences between the variable grid spacing method and the regular grid spacing method. First, ray segments calculated in the forward model cells should be divided into the larger cells of the inversion model. Second, the smoothness constraint equations needed to inhibit wild velocity variations need to be modified where the cells change in size. In a synthetic checkerboard reconstruction test with differently sized checkers the variable grid spacing method recovers the small and large checkers better than the regular grid spacing method in both two and three-dimensional test cases. For a real dataset, the variable grid spacing method distinguishes two low velocity zones better than the regular grid spacing method. Finally, it can be concluded that when the effort is spent to identify the regions in a model with best data coverage, the variable grid spacing method can produce velocity images with higher fidelity than when a uniform cell size is used. Especially, in many cases this method can enhance the fidelity of the shallow subsurface velocity distribution. In addition, variable grid spacing tomography can reduce the underdetermined regions in an inverse problem when the data coverage is irregular.

Place, publisher, year, edition, pages
2013. Vol. 48, no 3, 379-389 p.
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URN: urn:nbn:se:uu:diva-214314OAI: diva2:684716
Available from: 2014-01-08 Created: 2014-01-08 Last updated: 2014-01-08Bibliographically approved

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Tryggvason, Ari
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