This paper sets forth a novel approach for solving geospatial inversion problems using cutting edge techniques from statistics. Assume for instance that we want to obtain the mass density function in a given domain of interest from gravitational field measurements on the boundary of that domain. This is a well-studied and difficult problem. In all except a few special cases, the inverse problem has multiple solutions, and additional constraints (physical, or problem related) are needed to regularize it and to select a single, plausible solution.
LightOn || Google Scholar || LinkedIn ||@IgorCarron ||Homepage||ArXiv
+
Page Views on Nuit Blanche since July 2010
LightOn's Newsletter ||@LightOnIO (1162)||
LinkedIn (727)|| on CrunchBase || our Blog
Nuit Blanche community
@NuitBlog || Facebook (462) || Reddit (2452)
Compressive Sensing on LinkedIn (3967)
Advanced Matrix Factorization on Linkedin (1333)||
Nuit Blanche community
@NuitBlog || Facebook (462) || Reddit (2452)
Compressive Sensing on LinkedIn (3967)
Advanced Matrix Factorization on Linkedin (1333)||
Tuesday, July 22, 2008
CS: Gravimetric Detection by Compressed Sensing
Here is an inverse problem using sparsity as a constraint alone: Gravimetric Detection by Compressed Sensing by Marina Meila, Caren Marzban, Ulvi Yurtsever. The beginning of the article goes with:
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment