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DASCore: a Python Library for Distributed Fiber Optic Sensing.

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A new Python library, DASCore, addresses the immature open-source software ecosystem for distributed acoustic sensing (DAS) data. It facilitates analysis, visualization, and management of DAS data, supporting broader applications.

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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.