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 approximately 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.

Over the past decade, ObsPy, a python framework for seismology (Krischer et al., 2015), has become an integral part of many seismology research workflows. ObsPy provides parsers for most seismological data formats, clients for accessing data-centers, common signal processing routines, and event, station, and waveform data models. ObsPlus significantly expands ObsPy's functionality by providing simple data management abstractions and conversions between ObsPy classes and the ubiquitous pandas DataFrame (McKinney, 2010).

Monitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rockbursting, more safely and efficiently. Unfortunately, processing MIS data usually requires significant effort from human analysts, which can result in substantial costs and time commitments. The problem is exacerbated for operations that produce copious amounts of MIS, such as mines with high-stress and/or extraction ratios. Recently, deep learning methods have shown the ability to significantly improve the quality of automated arrival-time picking on earthquake data recorded by regional seismic networks. However, relatively little has been published on applying these techniques to MIS. In this study, we compare the performance of a convolutional neural network (CNN) originally trained to pick arrival times on the Southern California Seismic Network (SCSN) to that of human analysts on coal-mine-related MIS. We perform comparisons on several coal-related MIS data sets recorded at various network scales, sampling rates and mines. We find that the Southern-California-trained CNN does not perform well on any of our data sets without retraining. However, applying the concept of transfer learning, we retrain the SCSN model with relatively little MIS data after which the CNN performs nearly as well as a human analyst. When retrained with data from a single analyst, the analyst-CNN pick time residual variance is lower than the variance observed between human analysts. We also compare the retrained CNN to a simpler, optimized picking algorithm, which falls short of the CNN's performance. We conclude that CNNs can achieve a significant improvement in automated phase picking although some data set-specific training will usually be required. Moreover, initializing training with weights found from other, even very different, data sets can greatly reduce the amount of training data required to achieve a given performance threshold.

A catalog of explosion source parameters is valuable for testing methods of source classification in seismically active regions. We develop a manually reviewed catalog of explosions in the Utah region for 1 October 2012 to 30 June 2018 and use it to assess a newly proposed, magnitude-based depth discriminant. Within the Utah region we define 26 event clusters that are primarily associated with mine blasts but also include explosions from weapons testing and disposal. The catalog refinement process consists of confirming the explosion source labels, revising the local (ML) and coda duration (MC) magnitudes, and relocating the hypocenters. The primary features used to determine source labels are waveform characteristics such as frequency content, the proximity of the preliminary epicenter to a permitted blast region, the time of day, and prior notification from mine operators. We reviewed 2199 seismic events of which 1545 are explosions, 459 are local earthquakes, and 195 are other event types. Of the reviewed events, 127 (5.8%) were reclassified with new labels. Over 74% of the reviewed explosions have both ML and MC, a sizable improvement over the unreviewed catalog (65%). The mean MLMC value for the new explosion catalog is -0:196 +/- 0:017 (95% confidence interval) compared with a previously determined value of 0:048 +/- 0:008 for naturally occurring earthquakes in the Utah region. The shallow depths of the explosions lead to enhanced coda production, which in turn leads to anomalously large MC values. This finding confirms that ML-MC is a useful metric for discriminating explosions from deeper tectonic earthquakes in Utah. However, there is significant variation in MLMC among the 26 explosion source regions, suggesting that MLMC observations should be combined with other classification metrics to achieve the best performance in distinguishing explosions from earthquakes.