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.

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.