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Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection

Ward, Wil O.C.; Wilkinson, Paul B.; Chambers, Jon E.; Oxby, Lucy S.; Bai, Li

Authors

Wil O.C. Ward wcw@cs.nott.ac.uk

Paul B. Wilkinson pbw@bgs.ac.uk

Jon E. Chambers jecha@bgs.ac.uk

Lucy S. Oxby

Li Bai bai@cs.nott.ac.uk



Abstract

A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented.

Journal Article Type Article
Publication Date Apr 1, 2014
Journal Geophysical Journal International
Print ISSN 0956-540X
Publisher Oxford University Press (OUP)
Peer Reviewed Peer Reviewed
Volume 197
Issue 1
APA6 Citation Ward, W. O., Wilkinson, P. B., Chambers, J. E., Oxby, L. S., & Bai, L. (2014). Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection. Geophysical Journal International, 197(1), doi:10.1093/gji/ggu006
DOI https://doi.org/10.1093/gji/ggu006
Keywords Image processing; Neural networks, fuzzy logic; Tomography
Publisher URL http://gji.oxfordjournals.org/content/197/1/310
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
Additional Information This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





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