About this Research Topic
In recent decades, with the development of high-performance computing techniques, seismic full-waveform inversion (FWI) has been emerging as the most promising approach to reconstruct high-resolution subsurface model properties (e.g., velocity, density, and attenuation) for detecting natural resources, imaging deep Earth interior, monitoring carbon sequestration, and characterizing near-surface heterogeneities. However, due to ill posed nature and non-uniqueness of the inverse problem, large-scale practical applications of FWI have been impeded by a series of challenges. In this regard, we would like to bring together state-of-the-art studies on alternative misfits, advanced optimization methods, and optimized inversion strategies, to promote the development and applications of FWI.
The aim of this Research Topic is to present the latest advances of theories and methods for overcoming the difficulties in FWI, which have impeded its practical applications significantly. These difficulties encompass: (1) cycle-skipping problem due to lack of low-frequencies and poor initial models; (2) slow convergence rate; (3) unknown source signatures (e.g., wavelet, mechanism and location); (4) poor quality data set with strong noise; (5) extensive computation burden for large-scale three dimensional practices and applications; (6) multi-parameter trade-offs (or cross-talks) in elastic/anisotropic/viscous media; and (7) challenges for quantifying the uncertainties in mono-parameter and multi-parameter FWI.
This Research Topic invites submissions of Original Research and Review articles addressing the following themes that include, but are not limited to:
• Novel methods for reducing cycle-skipping problem in acoustic FWI
• Alternative misfit functions, e.g., travel time, envelope, and Wasserstein metric
• Advanced optimization methods, e.g., truncated (Gauss) Newton methods, Bayesian inference, and global optimization methods
• Source-independent algorithms for FWI
• Regularization techniques for solving ill posed problems
• New algorithms (e.g., simultaneous source) for accelerating FWI
• Sensitivity analysis and model parameterization choice in elastic/anisotropic FWI
• Reducing velocity and Q trade-offs in viscous FWI
• Imaging near-surface heterogeneities using surface-waves or early-arrivals with irregular topography
• Least-squares migration in acoustic/elastic/anisotropic/viscous media
• Novel methods for uncertainty quantification in mono-parameter and multi-parameter FWI
• Deep learning + FWI
The aim of this Research Topic is to present the latest advances of theories and methods for overcoming the difficulties in FWI, which have impeded its practical applications significantly. These difficulties encompass: (1) cycle-skipping problem due to lack of low-frequencies and poor initial models; (2) slow convergence rate; (3) unknown source signatures (e.g., wavelet, mechanism and location); (4) poor quality data set with strong noise; (5) extensive computation burden for large-scale three dimensional practices and applications; (6) multi-parameter trade-offs (or cross-talks) in elastic/anisotropic/viscous media; and (7) challenges for quantifying the uncertainties in mono-parameter and multi-parameter FWI.
This Research Topic invites submissions of Original Research and Review articles addressing the following themes that include, but are not limited to:
• Novel methods for reducing cycle-skipping problem in acoustic FWI
• Alternative misfit functions, e.g., travel time, envelope, and Wasserstein metric
• Advanced optimization methods, e.g., truncated (Gauss) Newton methods, Bayesian inference, and global optimization methods
• Source-independent algorithms for FWI
• Regularization techniques for solving ill posed problems
• New algorithms (e.g., simultaneous source) for accelerating FWI
• Sensitivity analysis and model parameterization choice in elastic/anisotropic FWI
• Reducing velocity and Q trade-offs in viscous FWI
• Imaging near-surface heterogeneities using surface-waves or early-arrivals with irregular topography
• Least-squares migration in acoustic/elastic/anisotropic/viscous media
• Novel methods for uncertainty quantification in mono-parameter and multi-parameter FWI
• Deep learning + FWI
Keywords: full-waveform inversion, seismic forward modeling, least-squares imaging, non-linearity, multi-parameter trade-offs, uncertainty quantification
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
