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ABSTRACT The ability to monitor seismicity and structural integrity of a mine using seismic noise can have great implication for detecting and managing ground-control hazards. The noise wavefield, however, is complicated by induced seismicity and heavy machinery associated with mining operations. In this study, we investigate the nature of time-dependent noise cross-correlations functions (CCFs) across an active underground longwall coal mine. We analyze one month of continuous data recorded by a surface 17 geophone array with an average station spacing of ∼200 m. To extract coherent seismic signals, we calculate CCFs between all stations for each 5-min window. Close inspection of all 5-min CCFs reveals waveforms that can be categorically separated into two groups, one with strong and coherent 1–5 Hz signals and one without. Using a reference station pair, we statistically isolate time windows within each group based on the correlation coefficient between each 5-min CCF and the monthly stacked CCF. The daily stacked CCFs associated with a high correlation coefficient show a clear temporal variation that is consistent with the progression of mining activity. In contrast, the daily stacked CCFs associated with a low correlation coefficient remain stationary throughout the recording period in line with the expected persistent background noise. To further understand the nature of the high correlation coefficient CCFs, we perform 2D and 3D back projection to determine and track the dominant noise source location. Excellent agreement is observed on both short (5-min) and long (daily) time scales between the CCF determined source locations, the overall migration of the active mining operation, and cataloged seismic event locations. The workflow presented in this study demonstrates an effective way to identify and track mining induced signals, in which CCFs associated with background noise can be isolated and used for further temporal structural integrity investigation.
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Improving signal-to-noise ratios of ambient noise cross-correlation functions using local attributes
SUMMARY For seismographic stations with short acquisition duration, the signal-to-noise ratios (SNRs) of ambient noise cross-correlation functions (CCFs) are typically low, preventing us from accurately measuring surface wave dispersion curves or waveform characteristics. In addition, with noisy CCFs, it is difficult to extract relatively weak signals such as body waves. In this study, we propose to use local attributes to improve the SNRs of ambient noise CCFs, which allows us to enhance the quality of CCFs for stations with limited acquisition duration. Two local attributes: local cross-correlation and local similarity, are used in this study. The local cross-correlation allows us to extend the dimensionality of daily CCFs with computational costs similar to global cross-correlation. Taking advantage of this extended dimensionality, the local similarity is then used to measure non-stationary similarity between the extended daily CCFs with a reference trace, which enables us to design better stacking weights to enhance coherent features and attenuate incoherent background noises. Ambient noise recorded by several broad-band stations from the USArray in North Texas and Oklahoma, the Superior Province Rifting EarthScope Experiment in Minnesota and Wisconsin and a high-frequency nodal array deployed in the northern Los Angeles basin are used to demonstrate the performance of the proposed approach for improving the SNR of CCFs.
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- Award ID(s):
- 2042098
- PAR ID:
- 10524319
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Geophysical Journal International
- Volume:
- 238
- Issue:
- 3
- ISSN:
- 0956-540X
- Format(s):
- Medium: X Size: p. 1470-1490
- Size(s):
- p. 1470-1490
- Sponsoring Org:
- National Science Foundation
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