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

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 com...

Full description

Saved in:
Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2010-10, Vol.7 (4), p.680-684
Main Authors: Kadkhodaie-Ilkhchi, Ali, Monteiro, Sildomar T, Ramos, Fabio, Hatherly, Peter
Format: Article
Language:English
Subjects:
Australia
Benchmarks
Blast holes
Boosting
Computational efficiency
Feeds
Fuzzy logic
Fuzzy systems
geological modeling
Geology
Iron
measurement-while-drilling (MWD)
Neural networks
Ores
pattern recognition
Recognition
Robotics and automation
Rock
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2010.2046312