Ordered clustering: a way to simplify analysis of multichannel signals

Rabiller, Philippe, Boles, Peter and Boashash, Boualem (2010). Ordered clustering: a way to simplify analysis of multichannel signals. In: 2010 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010). Proceedings. ISSPA 2010: 10th International Conference on Information Science, Signal Processing and their Applications, Kuala Lumpur, Malaysia, (237-242). 10-13 May, 2010. doi:10.1109/ISSPA.2010.5605482


Author Rabiller, Philippe
Boles, Peter
Boashash, Boualem
Title of paper Ordered clustering: a way to simplify analysis of multichannel signals
Conference name ISSPA 2010: 10th International Conference on Information Science, Signal Processing and their Applications
Conference location Kuala Lumpur, Malaysia
Conference dates 10-13 May, 2010
Proceedings title 2010 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010). Proceedings
Place of Publication Piscataway, NJ, USA
Publisher IEEE
Publication Year 2010
Sub-type Fully published paper
DOI 10.1109/ISSPA.2010.5605482
Open Access Status
ISBN 9781424471652
9781424471676
9781424471669
Start page 237
End page 242
Total pages 6
Language eng
Formatted Abstract/Summary
We describe here new possibilities offered by a clustering method routinely used by many petroleum companies and which could be used in other applications where analysis of multichannel signals has a significant role to play. In hydrocarbon exploration, the method is an efficient tool to condense a large number of logs (measurements performed on rocks, at a regular sampling rate, by running sensors along the borehole wall) into a single signal (rock facies) whose variation is geologically meaningful. The method is comprised of a clustering (MRGC) process and an autoordering (CFSOM) process, both of which are fully non-parametric. In the first step, the data structure is broken into clusters. In the second step, a ID chain of neurons is forced to fit the shortest path through the kernels of all clusters and passing only once through each kernel. Finally, each cluster is assigned an index whose value increases from one end of the chain to the other. As a result of ordering, the output signal is devoid of any "noise" due to the indexation of clusters which is performed arbitrarily or randomly by other methods and its variations truly reflect natural variations of all input signals taken jointly, and hence it can be subjected to signal analysis and processing techniques. The path through the kernels of clusters can be likened to the principal non-linear axis of the data structure.
Subjects 1706 Computer Science Applications
1710 Information Systems
1711 Signal Processing
Keyword Clustering methods
Logs
Array logs
Up-scaling
Array signal processing
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Collection: UQ Centre for Clinical Research Publications
 
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