Odd Kolbjørnsen: Super Resolution and Seismic Imaging - some historical lines and some current day applications
Super-resolution is a hot topic in current day Machine Learning. The origin of the methodology dates back to applications in seismic imaging. I discuss the evolution from the early days and highlight some papers which have given new theoretical insights along the way. I illustrate the bridge between traditional convex optimization and current day convolutional neural nets. Along the way I show some examples where we have used this for current day applications in seismic imaging.
Odd Kolbjørnsen has a Dr. Ing. in statistics from NTNU (2002) and has been working in the research institute sector (Norwegian Computing Center) and in the industry (Lundin Energy Norway) for a while. Since 2017 he is an Associate Professor at the Department of Mathematics, University of Oslo. His research interests include Seismic and EM inversion, Spatial statistics (Geostatistics), Bayesian methods, Super-resolution in seismic imaging and processing, and quantitative interpretation.