Chapter 12 - Application of artificial intelligence in distinguishing genuine microseismic events from the noise signals in underground mines
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{SHIRANIFARADONBEH:2024:AAIMGE,
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author = "Roohollah {Shirani Faradonbeh} and
Muhammad {Ghiffari Ryoza} and Mohammadali Sepehri",
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title = "Chapter 12 - Application of artificial intelligence in
distinguishing genuine microseismic events from the
noise signals in underground mines",
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editor = "Hoang Nguyen and Xuan-Nam Bui and Erkan Topal and
Jian Zhou and Yosoon Choi and Wengang Zhang",
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booktitle = "Applications of Artificial Intelligence in Mining and
Geotechnical Engineering",
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year = "2024",
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publisher = "Elsevier",
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pages = "197--220",
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keywords = "genetic algorithms, genetic programming, Microseismic
event, Noise signal, Underground mine, Machine
learning, Linear discriminant analysis,
Classification",
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isbn13 = "978-0-443-18764-3",
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URL = "http://hdl.handle.net/20.500.11937/95283",
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URL = "https://www.sciencedirect.com/science/article/pii/B9780443187643000084",
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DOI = "doi:10.1016/B978-0-443-18764-3.00008-4",
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abstract = "The discrimination of genuine microseismic events from
the noise signals during microseismic monitoring in
underground mines is critical to prevent
misinterpretations and correctly detect the highly
stressed zones prone to rockbursting. This study
proposes a novel mathematical classifier using genetic
programming (GP) algorithm to distinguish the recorded
signals in a coal mine. A database containing 100
recorded signals and six parameters representing the
spectrum and waveform characteristics of the signals
was employed for the modeling task. The hyperparameter
tuning was conducted through a systematic analysis to
find the best GP classifier. The classification
performance of the GP model was compared with that of
the linear discriminant analysis (LDA) technique based
on several statistical measures. By developing an
explicit mathematical model, the GP algorithm opened
the complex nature of the existing machine
learning-based classifiers and showed a higher
classification accuracy than LDA. The proposed model in
this study can be easily used to detect genuine
microseismic events and will help the engineers apply
the necessary controlling techniques to mitigate the
occurrence probability of catastrophic hazards",
- }
Genetic Programming entries for
Roohollah Shirani Faradonbeh
Muhammad Ghiffari Ryoza
Mohammadali Sepehri
Citations