4 papers
Ensemble Kalman Inversion for Geothermal Reservoir Modelling
Alex de Beer, Elvar K Bjarkason, Michael Gravatt +4
Numerical models of geothermal reservoirs typically depend on hundreds or thousands of unknown parameters, which must be estimated using sparse, noisy data. However, these models c…
Data Space Inversion for Efficient Predictions and Uncertainty Quantification for Geothermal Models
Alex de Beer, Andrew Power, Daniel Wong +7
The ability to make accurate predictions with quantified uncertainty provides a crucial foundation for the successful management of a geothermal reservoir. Conventional approaches…
Incorporating Posterior-Informed Approximation Errors into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification
Oliver J. Maclaren, Ruanui Nicholson, Elvar K. Bjarkason +2
We consider geothermal inverse problems and uncertainty quantification from a Bayesian perspective. Our main goal is to make standard, `out-of-the-box' Markov chain Monte Carlo (MC…
Pass-Efficient Randomized Algorithms for Low-Rank Matrix Approximation Using Any Number of Views
Elvar K. Bjarkason
This paper describes practical randomized algorithms for low-rank matrix approximation that accommodate any budget for the number of views of the matrix. The presented algorithms,…