Seismic monitoring of underground longwall mines can provide valuable information for managing coal burst risks and understanding the ground response to extraction. However, the underground longwall mine environment poses major challenges for traditional in-mine microseismic sensors including the restricted use of electronics due to potentially explosive atmospheres, the need to frequently and quickly relocate sensors as rapid mining progresses, and source parameter errors associated with complex time-dependent velocity structure. Distributed acoustic sensing (DAS), a technology that uses rapid laser pulses to measure strain along fiber-optic cables, shows potential to alleviate these shortcomings and improve seismic monitoring in coal mines when used in conjunction with traditional monitoring systems. Moreover, because DAS can acquire measurements that are not possible to record with traditional seismic sensors, it also enables entirely new monitoring approaches. This work demonstrates several DAS deployment strategies such as deploying fiber on the mine floor, in boreholes drilled from the surface and from mine level, on the longwall mining equipment, and wrapped around secondary support cans. Although there are several data processing and deployment improvements needed before DAS-based monitoring can become routine in underground longwall mines, the findings presented here can aid decision makers in assessing the potential of DAS to meet their needs and help guide future deployment designs. © 2025

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.

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.