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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