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Random-Objective Waveform Inversion of 3D-9C Shallow-Seismic Field Data

Pan, Yudi 1; Gao, Lingli 2; Bohlen, Thomas 1
1 Geophysikalisches Institut (GPI), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)


Robustness and uncertainty estimation are two challenging topics in full-waveform inversion (FWI). To overcome these challenges, we present the methodology of random-objective waveform inversion (ROWI), which adopts a multi-objective framework and a preconditioned stochastic gradient descent optimization algorithm. The use of one shot per iteration avoids using redundant data and reduces the computational cost. The Pareto solutions represent a group of most likely solutions and their differences quantifies the model uncertainty associated with the trade-off between conflicting objective functions. Due to the high dimensionality in the data and model spaces, it is prohibitively expensive to check the Pareto optimality of all solutions explicitly. Thus, we decompose the original multi-objective function into shot-related subproblems and use the Pareto solutions of the subproblems for trade-off analysis. We apply ROWI to a field multi-component shallow-seismic data set acquired in Rheinstetten, Germany. The 3D near-surface model is successfully reconstructed by ROWI and the main target, a refilled trench, is delineated. We compare the results estimated by ROWI and a conventional least squares FWI to prove the high efficiency of ROWI. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000138675
Veröffentlicht am 01.07.2022
DOI: 10.1029/2021JB022036
Zitationen: 12
Zitationen: 12
Cover der Publikation
Zugehörige Institution(en) am KIT Geophysikalisches Institut (GPI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2169-9313, 0148-0227, 2156-2202, 2169-9356
KITopen-ID: 1000138675
Erschienen in Journal of Geophysical Research: Solid Earth
Verlag John Wiley and Sons
Band 126
Heft 9
Seiten e2021JB022036
Nachgewiesen in Dimensions
Web of Science
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