Talks and Poster Presentations (with Proceedings-Entry):
C. Mecklenbräuker, P. Gerstoft, H. Yao:
"Bayesian Sparse Wideband Source Reconstruction of Japanese 2011 Earthquake";
Talk: 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing,
San Juan, Puerto Rico;
12-2011
- 12-16-2011; in: "2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing",
(2011),
273
- 276.
English abstract:
We consider the sparse inversion of seismic recordings from a Bayesian perspective. We have a prior belief that the spatially distributed seismic source should be sparse in the spatial domain. In a Bayesian framework, we assume a Laplace-like prior for a distributed wideband source and derive the corresponding objective function for minimization. We solve a sequence of convex minimization problems for finding a sparse seismic source representation from an underdetermined system of linear measurement equations using teleseismic P waves recorded by an array of sensors. The root mean square reconstruction error for the source distribution is evaluated through numerical simulations.
Keywords:
compressed sensing, sparsity, seismic, IRIS
Related Projects:
Project Head Gerald Matz:
Signal and Information Processing in Science and Engineering - Informationsnetze
Created from the Publication Database of the Vienna University of Technology.