Constrained optimization in seismic reflection tomography: a Gauss-Newton augmented Lagrangian approach

Seismic reflection tomography is a method for determining a subsurface velocity model from the traveltimes of seismic waves reflecting on geological interfaces. From an optimization viewpoint, the problem consists in minimizing a non-linear least-squares function measuring the mismatch between obser...

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Vydáno v:Geophysical journal international Ročník 164; číslo 3; s. 670 - 684
Hlavní autoři: Delbos, F., Gilbert, J. Ch, Glowinski, R., Sinoquet, D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford, UK Blackwell Publishing Ltd 01.03.2006
Blackwell Science Ltd
Oxford University Press (OUP)
Témata:
Algorithms
augmented Lagrangian
constrained optimization
Earth Sciences
Geology
least-squares approach
Mathematical models
Optimization
ray tracing
Reflection
Sciences of the Universe
Seismic engineering
Seismic phenomena
seismic reflection tomography
SQP algorithm
Tomography
augmented Lagrangian
least-squares approach
ray tracing
SQP algorithm
constrained optimization
seismic reflection tomography
ISSN:0956-540X, 1365-246X
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Shrnutí:Seismic reflection tomography is a method for determining a subsurface velocity model from the traveltimes of seismic waves reflecting on geological interfaces. From an optimization viewpoint, the problem consists in minimizing a non-linear least-squares function measuring the mismatch between observed traveltimes and those calculated by ray tracing in this model. The introduction of a priori information on the model is crucial to reduce the under-determination. The contribution of this paper is to introduce a technique able to take into account geological a priori information in the reflection tomography problem expressed as inequality constraints in the optimization problem. This technique is based on a Gauss-Newton (GN) sequential quadratic programming approach. At each GN step, a solution to a convex quadratic optimization problem subject to linear constraints is computed thanks to an augmented Lagrangian algorithm. Our choice for this optimization method is motivated and its original aspects are described. First applications on real data sets are presented to illustrate the potential of the approach in practical use of reflection tomography.
Bibliografie:ark:/67375/HXZ-5TFSMLWT-N
istex:93F9AF680166F01FFD011A34DECCAB452C411817
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ISSN:0956-540X
1365-246X
DOI:10.1111/j.1365-246X.2005.02729.x