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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. 

In the past decade, distributed acoustic sensing (DAS) has enabled many new monitoring applications in diverse fields including hydrocarbon exploration and extraction; induced, local, regional, and global seismology; infrastructure and urban monitoring; and several others. However, to date, the open-source software ecosystem for handling DAS data is relatively immature. Here we introduce DASCore, a Python library for analyzing, visualizing, and managing DAS data. DASCore implements an object-oriented interface for performing common data processing and transformations, reading and writing various DAS file types, creating simple visualizations, and managing file system-based DAS archives. DASCore also integrates with other Python-based tools which enable the processing of massive data sets in cloud environments. DASCore is the foundational package for the broader DAS data analysis ecosystem (DASDAE), and as such its main goal is to facilitate the development of other DAS libraries and applications. 

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.