Full Waveform Inversion With Extrapolated Low Frequency Data

TL;DR: A data-driven low-frequency recovery method based on deep learning from high-frequency signals to reconstruct the missing low- frequency signals more accurately and effectively is developed.

TL;DR: In this article, full waveform inversion (FWI) is used for modeling velocity structure by minimizing the misfit between recorded and predicted seismic waveforms, but the strong non-line waveforms are not considered.

TL;DR: The results show that the multiscale and dual-parameter inversion method proposed in this paper can provide reliable constraints, has better adaptability to noisy data, and can reliably and accurately reconstruct the dielectric properties distribution of the subsurface.

Abstract: Full waveform inversion is an effective tool for recovering the properties of the Earth from seismograms. However, it suffers from local minima caused mainly by the limited accuracy of the starting model and the lack of a low-frequency component in the seismic data. Because of the high velocity contrast between salt and sediment, the relation between the waveform and velocity perturbation is strongly nonlinear. Therefore, salt inversion can easily get trapped in the local minima. Since the velocity of salt is nearly constant, we can make the most of this characteristic with total variation regularization to mitigate the local minima. In this paper, we develop an adaptive primal dual hybrid gradient method to implement total variation regularization by projecting the solution onto a total variation norm constrained convex set, through which the total variation norm constraint is satisfied at every model iteration. The smooth background velocities are first inverted and the perturbations are gradually obtained by successively relaxing the total variation norm constraints. Numerical experiment of the projection of the BP model onto the intersection of the total variation norm and box constraints has demonstrated the accuracy and efficiency of our adaptive primal dual hybrid gradient method. A workflow is designed to recover complex salt structures in the BP 2004 model and the 2D SEG/EAGE salt model, starting from a linear gradient model without using low-frequency data below 3 Hz. The salt inversion processes demonstrate that wavefield reconstruction inversion with a total variation norm and box constraints is able to overcome local minima and inverts the complex salt velocity layer by layer.

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.

TL;DR: The author shows how stable finite difference operators can be derived to extrapolate acoustic wavefields in space and is widely applied in the petroleum industry in its effort to image subsurface seismic reflectors.

TL;DR: In this paper, a wave-equation traveltime inversion (WT-inversion) method is proposed to perturb the velocity model until the traveltimes from the synthetic seismograms are best fitted to the observed traveltimes in a least squares sense.

TL;DR: Inversion of seismic waveforms can be set up using least square methods as mentioned in this paper, and the inverse problem is then reduced to the problem of minimizing a lp;nonquadratic function in a space of many (104to106) variables.