Multipliers waveform inversion
Ali Gholami, Hossein S. Aghamiry, and St\'ephane Operto

TL;DR
This paper introduces a multipliers-based waveform inversion method that enhances full-waveform inversion (FWI) by improving convergence, robustness, and compatibility with existing FWI tools, especially when starting from inaccurate models.
Contribution
It formulates FWI as a constrained optimization problem solved via the augmented Lagrangian method, leading to a recursive algorithm that improves convergence and robustness.
Findings
Converges without low-frequency data from inaccurate initial models.
Requires minimal changes to existing FWI engines.
Demonstrates improved convergence and robustness in numerical experiments.
Abstract
The full-waveform inversion (FWI) addresses the computation and characterization of subsurface model parameters by matching predicted data to observed seismograms in the frame of nonlinear optimization. We formulate FWI as a nonlinearly constrained optimization problem, for which a regularization term is minimized subject to the nonlinear data matching constraint. Unlike FWI which is based on the penalty function, the method of multipliers solves the resulting optimization problems by using the augmented Lagrangian function; and leads to a two-step recursive algorithm. The primal step requires solving an unconstrained minimization problem like the traditional FWI with a difference that the data are replaced by the Lagrange multipliers. The dual step involves an update of the Lagrange multipliers. The overall performance of the algorithm is improved considering that this multiplier…
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Taxonomy
TopicsSeismic Imaging and Inversion Techniques · Seismic Waves and Analysis · Hydraulic Fracturing and Reservoir Analysis
