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Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

C. Mecklenbräuker, P. Gerstoft, H. Yao:
"Bayesian Sparse Wideband Source Reconstruction of Japanese 2011 Earthquake";
Vortrag: 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, San Juan, Puerto Rico; 12/2011 - 16.12.2011; in: "2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing", (2011), S. 273 - 276.



Kurzfassung englisch:
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.

Schlagworte:
compressed sensing, sparsity, seismic, IRIS


Zugeordnete Projekte:
Projektleitung Gerald Matz:
Signal and Information Processing in Science and Engineering - Informationsnetze