Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1190/GEO2013-0423.1
Titel (primär) Nonlinear joint inversion of tomographic data using swarm intelligence
Autor Paasche, H.; Tronicke, J.
Quelle Geophysics
Erscheinungsjahr 2014
Department MET
Band/Volume 79
Heft 4
Seite von R133
Seite bis R149
Sprache englisch
UFZ Querschnittsthemen RU5;
Abstract Geophysical techniques offer the potential to tomographically image physical parameter variations in the ground in two or three dimensions. Due to the limited number and accuracy of the recorded data, geophysical model generation by inversion suffers ambiguity. Linking the model generation process of disparate data by jointly inverting two or more data sets allows for improved model reconstruction. Fully nonlinear inversion using optimization techniques searching the solution space of the inverse problem globally enables quantitative assessment of the ambiguity inherent to the model reconstruction. We used two different multiobjective particle swarm optimization approaches to jointly invert synthetic crosshole tomographic data sets comprising radar and P-wave traveltimes, respectively. Beginning with a nonlinear joint inversion founded on the principle of Pareto optimality and game theoretic concepts, we obtained a set of Pareto-optimal solutions comprising commonly structured radar and P-wave velocity models for low computational costs. However, the efficiency of the approach goes along with some risk of achieving a final model ensemble not adequately illustrating the ambiguity inherent to the model reconstruction process. Taking advantage of the results of the first  pproach, we
inverted the database using a different nonlinear joint-inversion approach reducing the multiobjective optimization problem to a single-objective one. Computational costs were significantly higher, but the final models were obtained mutually independently allowing for objective appraisal of model parameter determination.
Despite the high computational effort, the approach was found to be an efficient nonlinear joint-inversion formulation compared to what could be extracted from individual nonlinear inversions of both data sets.
dauerhafte UFZ-Verlinkung
Paasche, H., Tronicke, J. (2014):
Nonlinear joint inversion of tomographic data using swarm intelligence
Geophysics 79 (4), R133 - R149 10.1190/GEO2013-0423.1