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Sparsity based denoising for continuously recorded seismic data


The main topics that will be addressed in this study include: (1)Formulate denoising as a wave propagation problem and transform this problem into a suitable sparse and redundant representation and then solve the corresponding sparse inversion for different kinds of seismic noise. (2)Study different sparsifying tranforms with the goal of deciding on one that optimally suits the denoising problem. (3)Develop algorithm(s) that benefit from using dual sensor streamer measurements (or more specifically the continuously recorded dual streamer measurements) (4)Implement denoising algorithm based on conclusions from 1), 2) and 3) with geophysical quality in mind. (5)Take algorithm(s) in 4) and make it viable for processing seismic data. That will entail parallelization of algorithm and develop/find optimized numerical solvers (High Performance Computing). (6)With the experience gained from the denoising project, establish whether the methods can be used or extended for data regularization, interpolation, and reconstruction.

Project leader: Walter Söllner

Started: 2015

Ends: 2018

Category: Næringsliv

Sector: Næringsliv

Budget: 1515000


Address: Oslo