Tristan van Leeuwen

TL;DR: In this paper, the objective function consists of a data-misfit term and a penalty term, which measures how accurately the wavefields satisfy the wave-equation, and the solution is forced to solve the waveequation and fit the observed data, which leads to significant computational savings.

TL;DR: In this paper, an extended full-waveform inversion formulation that includes general convex constraints on the model is proposed to steer free from parasitic local minima while keeping the estimated physical parameters laterally continuous and in a physically realistic range, and numerical experiments carried out on the challenging 2004 BP velocity benchmark demonstrate that these constraints improve the inversion result significantly by removing inversion artifacts, related to source encoding, and by clearly improved delineation of top, bottom, and flanks of a high-velocity high contrast salt inclusion.

TL;DR: It is shown that conventional optimization strategies are bound to outperform stochastic methods in the long run, and an optimization strategy that combines the benefits of both conventional and Stochastic optimization is reviewed.

TL;DR: A class of inverse problems in which the forward model is the solution operator to linear ODEs or PDEs is considered, which admits several dimensionality-reduction techniques based on data averaging or sampling, which are especially useful for large-scale problems.

TL;DR: In this article, the Gauss-Newton method was used to compute the update from random subsets of the data via curvelet-domain sparsity promotion, and two different subset sampling strategies were considered: randomized source encoding and drawing sequential shots firing at random source locations from marine data with missing near and far offsets.