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;
- 12-16-2011; in: "2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing",
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.
compressed sensing, sparsity, seismic, IRIS
Project Head Gerald Matz:
Signal and Information Processing in Science and Engineering - Informationsnetze
Created from the Publication Database of the Vienna University of Technology.