DASCore: a Python Library for Distributed Fiber Optic Sensing

Area of Science:

• Geophysics
• Data Science
• Software Engineering

Background:

• Distributed Acoustic Sensing (DAS) has seen widespread adoption in various fields like seismology and infrastructure monitoring over the last decade.
• The current open-source software ecosystem for handling DAS data is underdeveloped, hindering broader accessibility and application.
• There is a need for robust, user-friendly tools to process and manage the increasing volume of DAS data.

Purpose of the Study:

• To introduce DASCore, a novel Python library designed for the comprehensive analysis, visualization, and management of DAS data.
• To provide a foundational package that simplifies common DAS data processing tasks and file format handling.
• To foster the development of a more mature open-source ecosystem for DAS data analysis.

Main Methods:

• Development of DASCore, a Python library with an object-oriented interface for DAS data.
• Implementation of functionalities for data processing, transformation, file I/O (various DAS formats), and visualization.
• Integration with cloud computing tools for handling large-scale DAS datasets.

Main Results:

• DASCore offers a streamlined approach to analyzing, visualizing, and managing DAS data.
• The library supports reading and writing diverse DAS file types and includes tools for file system-based archives.
• DASCore integrates with existing Python tools, enabling scalable data processing in cloud environments.

Conclusions:

• DASCore significantly enhances the capabilities for working with distributed acoustic sensing data.
• As the foundational package for the DAS Data Analysis Ecosystem (DASDAE), it aims to accelerate the development of new DAS applications and libraries.
• This work promotes more accessible and efficient utilization of DAS technology across scientific and industrial domains.