Extracting information from drill data

被引:0
|
作者
Yin, K. [1 ]
Liu, H. [1 ]
Yang, H. [1 ]
机构
[1] College of Natural Resources, University of Minnesota, 322C Kaufert Laboratory, 2004 Folwell Ave., St. Paul, MN 55108, United States
关键词
Blasting - Design - Mining - Optimization - Rocks - Statistical methods - Strength of materials;
D O I
10.1080/13855140009408064
中图分类号
学科分类号
摘要
This paper is concerned with processing, analyzing and interpretating drill data. By converting the large amount of raw data into meaningful and manageable information, we aim to provide operators and mine engineers with timely advisable process-management decisions. Using multivariate statistical projection method, we have been able to extract two latent factors to illustrate the drilling process; and have developed a series of maps as easy-to-understand visual frameworks for collecting, organizing, displaying and evaluating data. Results of this research have the potential to increase productivity and to reduce production cost by improved blast design and optimized drilling processes.
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