A DASCore tutorial for CTEMPs 2025 workshop
This repository provides a gentle introduction to DASCore, a Python library for distributed fiber optic sensing. The 01_patch and 02_spool jupyter notebooks are shorter version of the ones on SEG 2024 tutorial with some updates, and the data exploratory analysis and processing in the 03_application jupyter notebook introduces new processing workflows for analyzing active source signals. The solutions to the exercises are provided in the 04_solutions notebook.
After completing the tutorial users should be able to:
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Use DASCore to index, query, and chunk a directory of DAS files.
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Create visualizations of DAS Patches in time and frequency domain.
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Perform filtering to improve signal to noise ratio of signal of interest.
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Low-pass filter and downsample the data.
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Calculate time-based statistics on rolling windows of continuous data.
The introductory slides can be found here.
You will also find four notebooks in this repo containing examples and exercises.
A DASCore version >= 0.1.9 is required.
Each of the notebooks has a "launch in collab" button you can use so you don't need to setup anything. However, if you wish to do this tutorial with a local installation of DASCore you can use the following section.
To install DASCore, you can use pip:
pip install "dascore"or conda/mamba (while also creating a virtual environment)
conda create -n dc_env dascoreor
mamba create -n dc_env dascoreNext, make sure jupyter notebook/lab is installed in your current environment then you are ready to open the notebooks.
jupyterSee the DASCore installation documentation for more details.