DASVader.jl

DASVader.jl is an open, fast, and flexible package for analyzing Distributed Acoustic Sensing (DAS) data in Julia. It is designed for ease of use, speed, and adaptability, making it ideal for processing large DAS datasets.

This README provides a brief overview of installing and using DASVader. Comprehensive documentation and examples will be available soon.

For the impatiens (like me), I am writting a quick and dirty intro to julia and DASVader.jl, get it here:

📄 Introduction to Julia and DASVader

Overview

DASVader is a framework designed to read, process, and visualize Distributed Acoustic Sensing (DAS) data, similar to how software like SAC (SAC - IRIS), PQL (PQL II), and SeisGram (SeisGram2K) handle more general seismological data.

The framework provides functionality for many typical signal processing steps in both the frequency and wavelength domains. DASVader leverages the excellent Seis.jl (Seis.jl GitHub) package for processing, with additional support from other packages like FFTW (FFTW.jl GitHub) and FourierAnalysis (FourierAnalysis.jl GitHub).

Plotting is a critical component of seismic data analysis, and DASVader enhances this by offering dynamic, interactive visualizations. This is accomplished using a customized version of InteractiveViz.jl (InteractiveViz.jl GitHub), allowing users to explore large datasets without worrying about performance.

Although DASVader is not intended for highly advanced processing techniques such as machine learning-based denoising, we are continuously working on improvements and new features. We welcome contributions, feedback, and feature requests from the community.

Key Features

• DAS data processing: First package in Julia dedicated to DAS processing.
• Dynamic visualization: Interactive plots for real-time data exploration.
• Open-source: Contributions are welcome!

Feel free to contribute or request new features, and help improve DASVader for the seismological community.

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Features

• Process large DAS datasets efficiently.
• Flexible tools for data visualization, transformation, and analysis.
• Designed with Julia’s high-performance capabilities.

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Installation

At present, DASVader is unregistered, and both it and its dependencies must be installed manually. Follow the steps below to get started:

1. Launch Julia from a terminal or your favorite IDE.

2. Enter Pkg mode by pressing ] in the Julia REPL.

3. Run the following command to add DASVader and its required dependencies:

   (v1.11) pkg> add https://github.com/marianoarnaiz/DASvader.jl https://github.com/anowacki/Geodesics.jl https://github.com/anowacki/Seis.jl

4. Once the installation is complete, you can start using DASVader by loading it into your Julia session:

   julia> using DASVader

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Usage

Step 0: Load the Package

using DASVader

Step 1: Read a raw Febus HDF5 file to memory.

Note: Only FEBUS A1 DAS is readable.

dDAS = rdas("SR_DS_2023-08-24_14-06-17_UTC.h5")

Step 2: View the data in the file.

You can change the colormap (cm) (e.g., :grays, :viridis, :RdBu_9) and adjust the color limits (climit) to your preference.

fig = viewdas(dDAS; cm=:RdBu_9, climit=10000)

Step 3: Write the figure to a PDF

savefig(fig, "Matrix.pdf")

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Some data

Notice that this data belongs to the FIMOPTIC projec!

If you need some data to test the code you can download this files. The run with the examples provided:

A Noisy blast file.

A file with a micro event.

A file with a big blast.

A file with something that might be an event.

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Contribution

Contributions, suggestions, and bug reports are welcome! Please feel free to contact me via email, open an issue or submit a pull request on the GitHub repository.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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“Use the force of fast and efficient DAS analysis with DASVader.jl.”