Dascore: A Python Library for Distributed Acoustic Sensing
In the past decade, distributed acoustic sensing (DAS) has found many monitoring applications in disparate areas such as hydrocarbon exploration and extraction; glaciology; hydrology; urban geophysics; induced, regional, and global seismology; and several more. Many of these DAS applications have proven transformative to geoscience research. However, the lack of mature open-source software for working with DAS data has resulted in redundant efforts to implement data input/output (IO) and processing routines among various research groups. To help more new users quickly begin working with DAS data, we developed DASCore, an open-source Python library for analyzing, managing, and visualizing DAS data. DASCore provides IO support for various DAS file formats and file system archives, common processing routines, and static visualizations. It implements an intuitive object-oriented interface, has an extensive automated test suite, and is comprehensively documented. DASCore is a foundational package for the broader DAS Data Analysis Ecosystem (DASDAE) and as such its main goal is to accelerate the development of other DAS applications.
Session: Advancing Seismology with Distributed Fiber Optic Sensing - I
Type: Oral
Room: K’enakatnu 6
Date: 5/3/2024
Presentation Time: 08:15 AM (local time)
Presenting Author: Derrick Chambers
Student Presenter: Yes
Additional Authors
Derrick Chambers Presenting Author Corresponding Author derrickchambers@mines.edu Colorado School of Mines |
Eileen Martin eileenrmartin@mines.edu Colorado School of Mines |
Ge Jin gjin@mines.edu Colorado School of Mines |
Ahmad Tourei tourei@mines.edu Colorado School of Mines |
Aaron Girard agirard@mines.edu Colorado School of Mines |
Ariel Lellouch ariellel@tauex.tau.ac.il Tel Aviv University |
Shihao Yuan syuan@mines.edu Colorado School of Mines |
Manuel Mendoza manuel.mendoza@colorado.edu University of Colorado Boulder |
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Dascore: A Python Library for Distributed Acoustic Sensing
Category
Advancing Seismology with Distributed Fiber Optic Sensing
Description