NDLI: Rock Recognition From MWD Data: A Comparative Study of Boosting, Neural Networks, and Fuzzy Logic

Content Provider	IEEE Xplore Digital Library
Author	Kadkhodaie-Ilkhchi, A. Monteiro, S.T. Ramos, F. Hatherly, P.
Copyright Year	2004
Abstract	Measurement-while-drilling (MWD) data recorded from drill rigs can provide a valuable estimation of the type and strength of the rocks being drilled. Typical MWD sensors include bit pressure, rotation pressure, pull-down pressure, pull-down rate, and head speed. This letter presents an empirical comparison of the statistical performance, ease of implementation, and computational efficiency associated with three machine-learning techniques. A recently proposed method, boosting, is compared with two well-established methods, neural networks and fuzzy logic, used as benchmarks. MWD data were acquired from blast holes at an iron ore mine in Western Australia. The boreholes intersected a number of rock types including shale, iron ore, and banded iron formation. Boosting and neural networks presented the best performance overall. However, from the viewpoint of implementation simplicity and computational load, boosting outperformed the other two methods.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	680
Ending Page	684
Page Count	5
File Size	426873
File Format	PDF
ISSN	1545598X
Volume Number	7
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Boosting Neural networks Fuzzy logic Geology Iron Australia Robotics and automation Ores Fuzzy systems Feeds pattern recognition fuzzy logic geological modeling measurement-while-drilling (MWD) neural networks
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

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