Journal Article10.1190/1.3073002
Simultaneous multifrequency inversion of full-waveform seismic data
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TL;DR: In this paper, a simultaneous multifrequency inversion approach for seismic data interpretation is presented, where a data weighting scheme balances the contributions from different frequency data components so the inversion process does not become dominated by highfrequency data components, which produces a velocity image with many artifacts.
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Abstract: We present a simultaneous multifrequency inversion approach for seismic data interpretation. This algorithm inverts all frequency data components simultaneously. A data-weighting scheme balances the contributions from different frequency data components so the inversion process does not become dominated by high-frequency data components, which produce a velocity image with many artifacts. A Gauss-Newton minimization approach achieves a high convergence rate and an accurate reconstructed velocity image. By introducing a modified adjoint formulation, we can calculate the Jacobian matrix efficiently, allowing the material properties in the perfectly matched layers (PMLs) to be updated automatically during the inversion process. This feature ensures the correct behavior of the inversion and implies that the algorithm is appropriate for realistic applications where a priori information of the background medium is unavailable. Two different regularization schemes, an L2 -norm and a weighted L2 -norm function, a...
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Citations
An overview of full-waveform inversion in exploration geophysics
Jean Virieux,Stéphane Operto +1 more
TL;DR: This review attempts to illuminate the state of the art of FWI by building accurate starting models with automatic procedures and/or recording low frequencies, and improving computational efficiency by data-compression techniquestomake3DelasticFWIfeasible.
Transdimensional tomography with unknown data noise
TL;DR: In this paper, a Hierarchical Bayesian inversion (HBIN) is proposed to deal with uncertainties in data noise in seismic tomography, where the level of noise in each data set, as well as the number of model parameters, are treated as unknowns in the inversion.
Regularized seismic full waveform inversion with prior model information
Amir Asnaashari,Romain Brossier,Stéphane Garambois,François Audebert,Pierre Thore,Jean Virieux +5 more
TL;DR: In this paper, the authors introduce three terms in the definition of the FWI misfit function: the data misfit itself, the first-order Tikhonov regularization term acting as a smoothing operator, and a prior model norm term.
206
Transdimensional inference in the geosciences
TL;DR: Concepts of transdimensional inference are introduced to a general readership and illustrate with particular seismological examples.
InversionNet: An Efficient and Accurate Data-Driven Full Waveform Inversion
Yue Wu,Youzuo Lin +1 more
TL;DR: In this paper, a convolutional neural network with an encoder-decoder structure was used to model the correspondence from seismic data to subsurface velocity structures, and a conditional random field (CRF) was employed on top of the CNN to generate structural predictions by modeling the interactions between different locations on the velocity model.
157
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