Completed projects

GAMES

Geophysics and Applied Mathematics in Exploration and Safe production used and developed methods of rock physics and geophysical modeling. These methods would then be embedded in a mathematical statistics framework that facilitates consistent prediction and uncertainty quantification from the available data and geological understanding.

The primary objectives were

• New understanding and mapping of geologic uplift offshore Norway
• New ways of understanding and interpreting continuous geophysical monitoring data using controlled and natural wave-field sources
• Develop robust and accurate methods for 3D elastic imaging of the subsurface

The project was supported by the Norwegian Research Council through the PETROMAKS2 framework, and by petroleum companies.

MASCOT

Maritime Autonomous Sampling and Control was an inter-disciplinary project to build on the science of statistical sampling for oceanographic applications with autonomous robots. The project aimed to address parts of the problems related to a pressing need for designing, implementing and testing algorithms for efficient sampling of the upper water-column, and to have the broader impact of commingling methods in statistics, oceanography and automated control including Artificial Intelligence (AI) for adaptive sampling.

The project was financed by the Norwegian Research Council through the IKT PLUSS programme, and led by professor Jo Eidsvik.

Transforming citizen science for biodiversity

Transforming citizen science for biodiversity was a multidisciplinary effort to improve the way citizens collect data on what they observe in the natural world, and to develop tools and methods to more effectively use citizen science data to address ecological questions.

It was part of the NTNU Digital Transformation initiative that supported groundbreaking ideas where digital technology and applied research is merged, the creation of more knowledge on digital transformation processes and the development of opportunities for spin-off projects and activities.

The project was led by professor Robert O'Hara.

Penalized Complexity-priors

Penalized Complexity-priors: A new tool to define default priors and robustify Bayesian models was a Norwegian Research Council supported project, and its main objective was to develop a new framework for specifying a class of default priors at model level. This would not only facilitate the ways priors are determined in practice but also robustify Bayesian analyses. The results were made available in the R-INLA package.

Knowledge Based Non-Stationary Modeling

The project was supported by the Norwegian Research Council and based on collaboration between researchers in statistics, quantitative genetics and hydrology. From a statisticians point of view, the goal was to develop statistical methods and knowledge for non-stationary processes.

The project contributed towards solving two important challenges: get better understanding about non-stationarity, and what data that are needed to detect it.

• Quantitative genetics challenge: Predict breeding values and identifying quantitative trait loci from SNP-panel data and pedigree information.
• Hydrology challenge: The problem of ungauged basins, i.e. challenge of estimating streamflow variables for locations where no streamflow observations are available.

Uncertainty in Reservoir Evaluation

Uncertainty in Reservoir Evaluation (URE) was supported by the Norwegian Research Council and a number of petroleum companies. The vision was to provide creative, mathematically based solutions to recognized challenges in reservoir evaluation, and to develop methodologies for analysis of spatial and spatio-temporal phenomena.