10 research outputs found
Inexact Augmented Lagrangian Method-Based Full-waveform Inversion with Randomized Singular Value Decomposition
Full Waveform Inversion (FWI) is a modeling algorithm used for seismic data
processing and subsurface structure inversion. Theoretically, the main
advantage of FWI is its ability to obtain useful subsurface structure
information, such as velocity and density, from complex seismic data through
inversion simulation. However, under complex conditions, FWI is difficult to
achieve high-resolution imaging results, and most of the cases are due to
random noise, initial model, or inversion parameters and so on. Therefore, we
consider an effective image processing and dimension reduction tool, randomized
singular value decomposition (rSVD) - weighted truncated nuclear norm
regularization (WTNNR), for embedding FWI to achieve high-resolution imaging
results. This algorithm obtains a truncated matrix approximating the original
matrix by reducing the rank of the velocity increment matrix, thus achieving
the truncation of noisy data, with the truncation range controlled by WTNNR.
Subsequently, we employ an inexact augmented Lagrangian method (iALM) algorithm
in the optimization to compress the solution space range, thus relaxing the
dependence of FWI and rSVD-WTNNR on the initial model and accelerating the
convergence rate of the objective function. We tested on two sets of synthetic
data, and the results show that compared with traditional FWI, our method can
more effectively suppress the impact of random noise, thus obtaining higher
resolution and more accurate subsurface model information. Meanwhile, due to
the introduction of iALM, our method also significantly improves the
convergence rate. This work indicates that the combination of rSVD-WTNNR and
FWI is an effective imaging strategy which can help to solve the challenges
faced by traditional FWI.Comment: 55 Pages, 21 Figure