Large Scale Inverse Problems. Computational Methods and Applications in the Earth Sciences

• Main Author: Freitag, Melina A (auth)
• Other Authors: Scheichl, Robert (auth), Cullen, Mike (auth), Kindermann, Stefan (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: De Gruyter 2013
• Series: Radon Series on Computational and Applied Mathematics
• Subjects: Geosciences; Data Assimilation; Regularization; Ill-Posed Inverse Problems; Optimization
• Online Access: DOAB: download the publication; DOAB: description of the publication

This book is thesecond volume of a three volume series recording the Radon Special Semester 2011 on Multiscale Simulation &amp Analysis in Energy and the Environment that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications. The solution of inverse problems is fundamental to a wide variety of applications such as weather forecasting, medical tomography, and oil exploration. Regularisation techniques are needed to ensure solutions of sufficient quality to be useful, and soundly theoretically based. This book addresses the common techniques required for all the applications, and is thus truly interdisciplinary. Thiscollection of surveyarticlesfocusses onthe large inverse problems commonly arising in simulation and forecasting in the earth sciences. For example, operational weather forecasting models have between 107 and 108 degrees of freedom. Even so, these degrees of freedom represent grossly space-time averaged properties of the atmosphere. Accurate forecasts require accurate initial conditions. With recent developments in satellite data, there are between 106 and 107 observations each day. However, while these also represent space-time averaged properties, the averaging implicit in the measurements is quite different from that used in the models. In atmosphere and ocean applications, there is a physically-based model available which can be used to regularise the problem. We assume that there is a set of observations with known error characteristics available over a period of time. The basi