E
Ernie Esser
Researcher at University of British Columbia
Publications - 17
Citations - 1021
Ernie Esser is an academic researcher from University of British Columbia. The author has contributed to research in topics: Inversion (meteorology) & Deconvolution. The author has an hindex of 8, co-authored 17 publications receiving 860 citations.
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Journal ArticleDOI
A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science
TL;DR: This work generalizes the primal-dual hybrid gradient (PDHG) algorithm to a broader class of convex optimization problems, and surveys several closely related methods and explains the connections to PDHG.
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Total Variation Regularization Strategies in Full-Waveform Inversion
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.
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Constrained waveform inversion for automatic salt flooding
TL;DR: In this paper, a robust full-waveform inversion code that includes constraints upon the set of allowable earth models is used to steer the inversion path away from local minima in its early stages.
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Low-cost time-lapse seismic with distributed compressive sensing — Part 1: Exploiting common information among the vintages
TL;DR: In this paper, the authors show that under ideal circumstances, in which errors related to taking measurements off the grid are ignored, high quality prestack data can be obtained from randomized subsampled measurements that are observed from surveys in which they choose not to revisit the same randomly subsampling on-the-grid shot locations.
Proceedings ArticleDOI
Fast "Online" Migration with Compressive Sensing
TL;DR: Comparison of the proposed method for sparse least-squares imaging shows a performance that rivals and even exceeds the performance of state-of-the art one-norm solvers that are able to carry out least-Squares migration at the cost of a single migration with all data.