Application of Regional Subspace Detection to Identify Mining Related Seismicity

Details

• Personal Author:
  Chambers DJA ; Koper KD ; Mccarter MK ; Pankow KL
• Description:
  In a mining environment, high quality seismic event catalogs are often useful in interpreting local stress states and identifying areas with elevated ground control risk. Installation, operation, and maintenance of local/in-mine seismic networks, however, may be prohibitively expensive, and mine operators may not anticipate the need for seismic data until unusual mining conditions arise. If regional, generally public, seismic instrumentation could be used to reproduce the results generated by local/in-mine instrumentation to an acceptable degree of fidelity, it would be a valuable and inexpensive tool for mine planners and ground control engineers. In this study we apply subspace detection-a non-traditional seismic event detection technique-to data from a sparse, regional network. The process is employed to identify mining induced seismicity (MIS) from an underground coal district. The results are compared to a seismic event catalog produced by a local seismic array operating within the permit boundaries. Additionally, we analyze the performance of two different stations of the same sparse, regional network in identifying blasts at a surface coal mine for which we have a detailed blasting log. In both cases data from stations between 10 and 60 km away from mining activity are used. We also explore the trade-offs between the number of detections and the number of false positives, which is influenced heavily by detection thresholds and the number of stations used in the process. For the surface mine we are able to identify more than 90% of the production blasts documented in the blasting log. We also find that the detected blasts from each of the four active pits produce waveforms similar to one another. The results are not dependent on source-receiver distance, and by requiring detections to occur on both stations the number of sure false positives is reduced to zero. In the underground mining district we identify between 30% and 75% of the events detected by the local array, depending on the acceptable number of false positives and the source-receiver distance. We also find many events that were missed by the local array, showing that regional subspace detection can usefully augment traditional local catalogs. Moreover, subspace detection can provide an estimate on event location and classification using data from as little as one seismic station. [Description provided by NIOSH]
• Subjects:
  Computers Electrical Equipment And Supplies Engineering Manufacturing And Industrial Facilities Mining Occupational Health Safety Software
• Keywords:
  Coal-mining Ground-control Ground-stability Mining-industry Models Monitoring-systems Risk-factors Underground-mining
• Series:
  Mining Publications
• Publisher:
  West Virginia University
• Document Type:
  Text
• Genre:
  Proceedings
• Place as Subject:
  OSHA Region 3 ; OSHA Region 8 ; Pennsylvania ; Utah ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  OMSHR - Office of Mine Safety and Health Research
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  293-299
• NIOSHTIC Number:
  nn:20047052
• Citation:
  Proceedings of the 34th International Conference on Ground Control in Mining, July 28-30, 2015, Morgantown, West Virginia. Barczak T, Peng SS, Schmidt D, Tadolini S, Thompson M, eds. Morgantown, WV: West Virginia University, 2015 Jul; :293-299
• Editor(s):
  Barczak T; Peng SS; Schmidt D; Tadolini S; Thompson M
• Federal Fiscal Year:
  2015
• Peer Reviewed:
  False
• Source Full Name:
  Proceedings of the 34th International Conference on Ground Control in Mining, July 28-30, 2015, Morgantown, West Virginia
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:7053e5e9092732e58c9640fe2b711e7abd9d43358f0b8b45c9cd8478f37c9b6ad8f258c980e238a17f1a6ab9dd2bee7ec3cf4aedcfd04a5afaee3715df85e049
• Download URL:
  https://stacks.cdc.gov/view/cdc/228191/cdc_228191_DS1.pdf
• File Type:
  [PDF - 1.58 MB ]