Application of artificial intelligence in distinguishing genuine microseismic events from the noise signals in underground mines

QR Code

Application of artificial intelligence in distinguishing genuine microseismic events from the noise signals in underground mines

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 genet...

Full description

Bibliographic Details
Main Authors:	Shirani Faradonbeh, Roohollah, Ghiffari Ryoza, Muhammad, Sepehri, Mohammadali
Other Authors:	Hoang Nguyen
Format:	Book Chapter
Published:	Elsevier 2024
Subjects:	Business & Economics
Online Access:	http://hdl.handle.net/20.500.11937/95283

Similar Items

Practical Models To Distinguish Between Seismic Events And Blast Signals
by: Shirani Faradonbeh, Roohollah, et al.
Published: (2023)

Source identification of microseismic events in underground mines with interferometric imaging and cross wavelet transform
by: Huang, L., et al.
Published: (2018)

Harnessing machine learning for seismic event discrimination in deep underground mining: a case study from Western Australia
by: Shirani Faradonbeh, Roohollah, et al.
Published: (2024)

Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms
by: Chimunhu, Prosper, et al.
Published: (2025)

Development of Novel Hybrid Intelligent Predictive Models for Dilution Prediction in Underground Sub-level Mining
by: Chimunhu, Prosper, et al.
Published: (2024)

Relocation method of microseismic source in deep mines
by: Huang, L., et al.
Published: (2016)

Monitoring blasting events in an underground mine with artificial intelligence techniques
by: Huang, L., et al.
Published: (2017)

The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility
by: Chimunhu, Prosper, et al.
Published: (2024)

Waveform inversion for microseismic velocity analysis and event location in VTI media
by: Michel, O., et al.
Published: (2017)

Predicting overbreak and underbreak in underground longhole stoping using meta-soft computing models
by: Taheri, Sina, et al.
Published: (2025)

Exploring digital twin systems in mining operations: A review. Green and Smart Mining Engineering
by: Nobahar, Pouya, et al.
Published: (2024)

Reservoir imaging using induced microseismicity
by: Al Ramadhan, Abdullah Ali S
Published: (2010)

Failure response of rocks under different cyclic loading histories
by: Taheri, Abbas, et al.
Published: (2023)

A study on the characteristics of microseismic signal during deformation and failure of different materials under uniaxial compression
by: Li, C., et al.
Published: (2016)

Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence
by: Baghbani, Abolfazl, et al.
Published: (2023)

Slope Stability Monitoring Methods and Technologies for Open-Pit Mining: A Systematic Review
by: Le Roux, Rohan, et al.
Published: (2025)

The role of geological features in mine seismicity: Kanowna Belle case study
by: Moreira de Oliveira, Afranio L, et al.
Published: (2024)

Underground mining and water management
by: Moran, Chris, et al.
Published: (2011)

The adequacy of genuine modal realism
by: Noonan, Harold
Published: (2013)

Using Artificial Neural Networks (ANNs) to Predict Stope Overbreak at Plutonic Underground Gold Mine
by: Boxwell, D., et al.
Published: (2014)

Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning
by: Huang, L., et al.
Published: (2018)

Sensors Utilisation and Data Collection of Underground Mining
by: Moghaddam, Fatemeh
Published: (2022)

Risk-based evaluation for underground mine planning
by: Maybee, Bryan, et al.
Published: (2011)

Integrated Optimization of Underground Mine Design and Scheduling
by: Little, J, et al.
Published: (2013)

Evaluation of Monorail Haulage in Metalliferous Underground Mining
by: Chanda, Emmanuel, et al.
Published: (2009)

Toward optimum value in underground mine scheduling
by: Maybee, Bryan, et al.
Published: (2010)

Quantifying genuine multipartite correlations and their pattern complexity
by: Girolami, Davide, et al.
Published: (2017)

Theory of genuine tripartite nonlocality of Gaussian states
by: Adesso, Gerardo, et al.
Published: (2014)

Converting multilevel nonclassicality into genuine multipartite entanglement
by: Regula, Bartosz, et al.
Published: (2018)

Subsidence prediction and mine design for underground coal mining in the Collie Basin
by: Misich, Ian J.
Published: (1997)

A Case Study of Prediction and Analysis of Unplanned Dilution in an Underground Stoping Mine using Artificial Neural Network
by: Jang, Hyong Doo, et al.
Published: (2014)

Underground Blasting Optimization by Artificial Intelligence Techniques
by: Jang, Hyong Doo, et al.
Published: (2013)

Development of wireless sensor networks for underground communication and monitoring systems (the cases of underground mine environments)
by: Moridi, M., et al.
Published: (2018)

Using underground blast monitoring to indicate rock mass anisotropy at two Australian underground mines
by: Fleetwood, Kelly, et al.
Published: (2008)

Permeability inversion using induced microseismicity: A case study for Longmaxi shale gas reservoir
by: Zhang, Y., et al.
Published: (2020)

Evaluation of monorail haulage systems in metalliferous underground mining
by: Besa, Bunda
Published: (2010)

Explosion risk assessment model for underground mine atmosphere
by: Cheng, J., et al.
Published: (2017)

Health challenges facing underground hard-rock mining
by: Jones, Ifan
Published: (2015)

Risk Assessment of Occupational Injuries in Underground Coal Mines
by: Ghosh, Apurna
Published: (2010)

Impact of development strategies on the value of underground mining projects
by: Maybee, Bryan, et al.
Published: (2009)

Cannot write session to /tmp/vufind_sessions/sess_c4nao3p46fm2nqls3icdslljjt