An Autoencoder-Based Deep Learning Model for Enhancing Noise Characterization and Microseismic Event Detection in Underground Longwall Coal Mines Using Distributed Acoustic Sensing Monitoring

Details

• Personal Author:
  Ankamah AT ; Chambers DJA ; Hole JA ; Martin ER ; Tourei A
• Description:
  The longwall mining method is designed to optimize coal extraction through controlled roof caving, which inevitably induces seismicity. This research employs a distributed acoustic sensing (DAS) system incorporating a fire-safe fiber-optic cable strategically installed underground within an operational longwall coal mine. Despite lower sensitivity than traditional seismometers, DAS sensing technology benefits from dense sensor spacing and close proximity to the active face, where many microseismic events occur. To automatically detect seismic events within the voluminous DAS data records, we employ convolutional autoencoder deep learning models that can be used for anomaly (potential seismic event) detection in power spectral density (PSD) images of DAS recordings. The kernel density estimation (KDE) technique is used to calculate the probability density function (PDF) for the density scores of the latent space (representation of compressed data). We then use this calculated parameter as a threshold to distinguish between the PSD associated with background noise and with potential seismic events. The DAS monitoring system in conjunction with the developed deep learning model could enhance longwall coal mining safety and efficiency by offering valuable data from its densely deployed multichannel sensors near mining operations. [Description provided by NIOSH]
• Subjects:
  Geology Mining Occupational Health Safety
• Keywords:
  Longwall Mining Mining Engineering Rock Mechanics Seismic Monitoring Underground Mines
• Series:
  Mining Publications
• ISBN:
  9780979497599
• Publisher:
  American Rock Mechanics Association (ARMA)
• Document Type:
  Text
• Genre:
  Proceedings
• Place as Subject:
  Colorado ; OSHA Region 10 ; OSHA Region 3 ; OSHA Region 8 ; Virginia ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  SMRD - Spokane Mining Research Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• NIOSHTIC Number:
  nn:20070424
• Citation:
  58th US Rock Mechanics/Geomechanics Symposium, June 23-26, 2024, Golden, Colorado. Alexandria, VA: American Rock Mechanics Association (ARMA), 2024 Jun; :ARMA-24-0207
• Federal Fiscal Year:
  2024
• NORA Priority Area:
  Mining
• Peer Reviewed:
  False
• Source Full Name:
  58th US Rock Mechanics/Geomechanics Symposium, June 23-26, 2024, Golden, Colorado
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:6ad0cf187283d27aac4a1543fd194d4bfc457b3d387cb48892f044bca38e36eb952251faa2efe26deb7ca7f8efdd90c7132c96cb783786407f637865dacec70e
• Download URL:
  https://stacks.cdc.gov/view/cdc/215705/cdc_215705_DS1.pdf
• File Type:
  [PDF - 644.17 KB ]