Marchenko Inversion

Master Thesis (2018)
Author(s)

S.B. Demir (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

Evert Cornelis Slob – Mentor

B. Yang – Mentor

Florian Wellmann – Graduation committee member

Faculty
Civil Engineering & Geosciences
Copyright
© 2018 Semih Demir
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Semih Demir
Graduation Date
24-08-2018
Awarding Institution
Delft University of Technology, ETH Zürich, RWTH Aachen University
Programme
['Applied Geophysics | IDEA League']
Faculty
Civil Engineering & Geosciences
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Abstract

Marchenko inversion is a new way to invert seismic or electromagnetic data recorded during geophysical surveys. The inversion method uses Marchenko theory. This is a recent development which enables the retrieval of Green's functions at any place in the subsurface. A non-recursive Marchenko inversion method has already been introduced but in this thesis a recursive Marchenko inversion method is implemented and analysed. A recursive scheme lies at the center of this new method. In this thesis, the new method is implemented and tested on a 1D subsurface model. The recursive scheme is first validated. This is done by computing a reflection response with it and comparing it with a reflection response resulting from forward modeling. After this, the accuracy of retrieved local reflection coefficients from the recursive inversion method is determined. This is done by comparison with exact reflection coefficients of the subsurface model. After this, several different parameters of the used subsurface model, data computation and the recursive inversion method itself are investigated for their influence on the accuracy of the inversion method. In particular interest is the effect of interval time errors because these result in errors that can build up rapidly through the recursion. However, the method has a big advantage. It is shown that the recursive Marchenko inversion method has a way to retrieve the magnitude of made interval time errors and correct for these when interval times are overestimated. In this way the error build up is stopped. In the end, it is shown that the new method delivers high accuracy results and has an advantage in computational expense compared to the existing recursive Marchenko inversion method. It is concluded that the new method shows promising prospects and that it is worthwhile to investigate the method further.

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