https://github.com/DAS-RCN/awesome-das
A curated list of DAS tools and resources.
https://github.com/DAS-RCN/awesome-das
List: awesome-das
awesome-list das distributed-acoustic-sensing seismology
Last synced: 4 months ago
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A curated list of DAS tools and resources.
- Host: GitHub
- URL: https://github.com/DAS-RCN/awesome-das
- Owner: DAS-RCN
- License: cc0-1.0
- Created: 2021-08-27T13:23:08.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-11-04T13:42:37.000Z (over 2 years ago)
- Last Synced: 2024-01-04T17:34:30.573Z (over 1 year ago)
- Topics: awesome-list, das, distributed-acoustic-sensing, seismology
- Language: Ruby
- Homepage:
- Size: 27.3 KB
- Stars: 21
- Watchers: 2
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-seismology - awesome-das - Curated list of awesome resources for distributed acoustic sensing (DAS). (Fibre optic sensing)
README
# Awesome DAS [](https://awesome.re)
> A curated list of awesome resources for distributed acoustic sensing (DAS).
## Contents
* [Data Management](#data-management)
* [Data repositories](#data-repositories)
* [Processing](#processing)
* [Visualisation](#visualisation)
* [Modelling](#modelling)
## Data Management
* [das-convert](https://git.pyrocko.org/pyrocko/das-convert) - Convert and downsample DAS data sets efficiently to established seismological data formats.
* [dastools](https://git.gfz-potsdam.de/javier/dastools) - tools to read, manipulate and convert seismic waveforms generated by DAS systems.
## Processing
* [distpy](https://github.com/Schlumberger/distpy) - An Open Source python module for rapid prototyping Distributed Acoustic Sensing (DAS) processing flows
* [lightguide](https://github.com/pyrocko/lightguide) - Tools and modelling for distributed acoustic sensing data. Advanced de-noising filtering techniques. Integrates into Pyrocko.
* [mldas](https://github.com/DAS-RCN/mldas) - Machine Learning for distributed acoustic sensing.
* [jDAS](https://github.com/martijnende/jDAS) - Coherence-based Deep Learning denoising of DAS data.
## Visualisation
* [pyrocko](https://pyrocko.org) - Pyrocko's snuffler can handle large DAS data sets efficiently and visualize waterfall plots interactively.
## Modelling
* [pyrocko.gf](https://pyrocko.org) - Pyrocko-GF can forward model strain and strain-rate along the fiber's trajectory.
## Data repositories
List of public DAS data repositories:
* PoroTomo Experiment at Brady Hot Springs ([GDR OpenEI](https://gdr.openei.org/submissions/849))
* FORGE Phase 2C ([GDR OpenEI](https://gdr.openei.org/submissions/1185))
* Garner Valley ([GDR OpenEI](https://gdr.openei.org/submissions/614))
* Belgium DAS ([Caltech](https://data.caltech.edu/records/1296))
* Monterrey Bay Dark Fiber ([GitHub](https://github.com/njlindsey/Photonic-seismology-in-Monterey-Bay-Dark-fiber1DAS-illuminates-offshore-faults-and-coastal-ocean))
* RCA Shore Station outward along the Cascadia Margin ([Announcement/FTP Link](https://oceanobservatories.org/2022/02/distributed-acoustic-sensing-lays-groundwork-for-earthquake-tsunami-warnings-and-more/))
* PubDAS a PUBlic Distributed Acoustic Sensing datasets repository for geosciences ([Globus] (https://app.globus.org/file-manager?origin_id=706e304c-5def-11ec-9b5c-f9dfb1abb183&origin_path=%2F))
Selected earthquake data:
* SAFOD DAS array ([GitHub](https://github.com/ariellellouch/DASDetection))
* Stanford Phase 1 experiment ([GitHub](https://github.com/eileenrmartin/FiberOpticEarthquakes))
* Fairbanks Farmers Loop ([GitHub](https://github.com/eileenrmartin/FiberOpticEarthquakes))
