Pragmatic algorithms for implementing geostatistics with large datasets


With the ability to collect and store increasingly large datasets on modern computers comes the need to be able to process the data in a way that can be useful to a Geostatistician or application scientist. Although the storage requirements only scale linearly with the number of observations in the dataset, the computational complexity in terms of memory and speed, scale quadratically and cubically respectively for likelihood-based Geostatistics. Various methods have been proposed and are extensively used in an attempt to overcome these complexity issues. This thesis introduces a number of principled techniques for treating large datasets with an emphasis on three main areas: reduced complexity covariance matrices, sparsity in the covariance matrix and parallel algorithms for distributed computation. These techniques are presented individually, but it is also shown how they can be combined to produce techniques for further improving computational efficiency.

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Institution: Aston University
Uncontrolled Keywords: Pragmatic algorithms,implementing geostatistics,large datasets
Last Modified: 08 Dec 2023 08:37
Date Deposited: 07 Feb 2011 11:29
Completed Date: 2008
Authors: Ingram, Benjamin R.


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