Hostname: page-component-669899f699-ggqkh Total loading time: 0 Render date: 2025-04-27T12:42:29.804Z Has data issue: false hasContentIssue false
  • English
  • Français

Data analysis toolkit for long-term, large-scale experiments

Published online by Cambridge University Press:  05 July 2018

D. P. Bennett ,
R. J. Cuss ,
P. J. Vardon ,
J. F. Harrington ,
R. N. Philp  and
H. R. Thomas
D. P. Bennett*
Affiliation:
Geoenvironmental Research Centre, Cardiff School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK
R. J. Cuss
Affiliation:
British Geological Survey, Keyworth, Nottingham NG12 5GG, UK
P. J. Vardon
Affiliation:
Geoenvironmental Research Centre, Cardiff School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK Geo-Engineering Section, Department of Geoscience and Engineering, Delft University of Technology, PO Box 5048, 2600 GA Delft, The Netherlands
J. F. Harrington
Affiliation:
British Geological Survey, Keyworth, Nottingham NG12 5GG, UK
R. N. Philp
Affiliation:
Geoenvironmental Research Centre, Cardiff School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK
H. R. Thomas
Affiliation:
Geoenvironmental Research Centre, Cardiff School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK
*
*E-mail: bennettdp@cardiff.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A new data analysis toolkit which is suitable for the analysis of large-scale, long-term datasets and the phenomenon/anomalies they represent is described. The toolkit aims to expose and quantify scientific information in a number of forms contained within a time-series based dataset in a quantitative and rigorous manner, reducing the subjectivity of observations made, thereby supporting the scientific observer. The features contained within the toolkit include the ability to handle non-uniform datasets, time-series component determination, frequency component determination, feature/event detection and characterization/parameterization of local behaviours. An application is presented of a case study dataset arising from the 'Lasgit' experiment.

Keywords

lasgitlarge-scale experimentlarge datasetnon-uniformtime-series analysis
Type
Research Article
Information
Mineralogical Magazine , Volume 76 , Issue 8 , December 2012 , pp. 3355 - 3364
Creative Commons
Creative Common License - CCCreative Common License - BY
© [2012] The Mineralogical Society of Great Britain and Ireland. This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Mineralogical Society of Great Britain and Ireland 2012

References

Bagchi, S. and Mitra, S.K. (1999) The Nonuniform Discrete Fourier Transform and its Applications in Signal Processing. Kluwer Academic Publishers, Dordrecht, The Netherlands.CrossRefGoogle Scholar
Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis: Forecasting and Control. Revised Edition. Holden-Day, San Francisco, California, USA. Chatfield, C. (1989) The Analysis of Time Series: An Introduction. Fourth Edition. Chapman & Hall, London.Google Scholar
Cuss, R.J., Harrington, J.F. and Noy, D.J. (2010) Large Scale Gas Injection Test (Lasgit) Performed at the Ä spöHard Rock Laboratory: Summary Report 2008. SKB Technical report TR-10–38. Swedish Nuclear Fuel and Waste Management Company, Stockholm.Google Scholar
Cuss, R.J., Harrington, J.F. and Noy, D.J., Wikman, A. and Selin, P. (2011) Large scale gas injection test (Lasgit): results from two gas injection tests. Physicsand Chemistry of the Earth, 36, 1729–1742.CrossRefGoogle Scholar
Dixon, D.A., Chandler, N., Graham, J. and Gray, M.N. (2002) Two large-scale sealing tests conducted at Atomic Energy of Canada’s underground research laboratory: the buffer-container experiment and the isothermal test. Canadian Geotechnical Journal, 39, 503–518.CrossRefGoogle Scholar
Golub, G.H. and van der Vorst, H.A. (2000) Eigenvalue computation in the 20th century. Journal of Computational and Applied Mathematics, 123, 35–65.CrossRefGoogle Scholar
Golyandina, N., Nekrutkin, V. and Zhigljavsky, A. (2001) Analysis of Time Series Structure: SSA and Related Techniques. Chapman & Hall/CRC, Boca Raton, Florida, USA.CrossRefGoogle Scholar
Halpern, S. (1978) The Assurance Science: An Introduction to Quality Control and Reliability. Prentice-Hall, Englewood Cliffs, New Jersey, USA.Google Scholar
O’Connor, P.D.T. (1995) Practical Reliability Engineering. Third Edition Revised. John Wiley & Sons, Chichester, UK.Google Scholar
SKB (2006) Long-term Safety for KBS-3 Repositories at Forsmark and Laxemar - A First Evaluation: Main Report of the SR-Can Project. SKB Technical Report TR-06–09 Swedish Nuclear Fuel and Waste Management Company, Stockholm.Google Scholar
SKB (2007) RD&D Programme 2007: Programme for Research, Development and Demonstration of Methods for the Management and Disposal of Nuclear Waste. SKB Technical Report TR-07–12. Swedish Nuclear Fuel and Waste Management Company, Stockholm.Google Scholar