https://github.com/martijnende/jDAS
Coherence-based Deep Learning denoising of DAS data
https://github.com/martijnende/jDAS
Last synced: 4 months ago
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Coherence-based Deep Learning denoising of DAS data
- Host: GitHub
- URL: https://github.com/martijnende/jDAS
- Owner: martijnende
- License: mit
- Created: 2021-08-21T13:35:25.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-10-12T17:13:36.000Z (over 2 years ago)
- Last Synced: 2023-05-15T14:23:30.540Z (almost 2 years ago)
- Language: Python
- Size: 14.5 MB
- Stars: 12
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Learning denoising of DAS data
[](https://jdas.readthedocs.io/en/latest/?badge=latest)
--------------
Contents: [overview](#overview) | [example](#example) | [quickstart](#quickstart) | [5-minute explainer video](#explainer-video) | [citing _jDAS_](#citing-jdas)
--------------
## Overview
_jDAS_ is a self-supervised Deep Learning model for denoising of Distributed Acoustic Sensing (DAS) data. The principle that underlies _jDAS_ is that spatio-temporally coherent signals can be interpolated, while incoherent noise cannot. Leveraging the framework laid out by Batson & Royer ([2019; ICML](http://arxiv.org/abs/1901.11365)), _jDAS_ predicts the recordings made at a target channel using the target's surrounding channels. As such, it is a self-supervised method that does not require "clean" (noise-free) waveforms as labels.
Retraining the model on new data is quick and easy, and will produce an optimal separation between coherent signals and incoherent noise for your specific dataset:
```
from jDAS import JDAS
jdas = JDAS()
data_loader = jdas.init_dataloader(data)
model = jdas.load_model()
model.fit(data_loader, epochs=50)
```
Denoising your data is then done through a single function call:
```
clean_data = JDAS.denoise(data)
```
That's all!
For a more detailed description of the methods, see the [documentation](https://jdas.readthedocs.io/). In-depth examples on *jDAS* denoising and retraining are provided in the `examples` directory.
--------------
## Example
The example below is taken from a submarine DAS experiment conducted offshore Greece. At around 25 seconds and earthquake hits the DAS cable and induces a spatio-temporally coherent strain field. _jDAS_ removes the incoherent background noise while keeping the earthquake signals.
Note that some aliasing artifacts have been introduced in rendering this static JPEG. A code example to reproduce this figure is included in the `examples` directory of the project.
--------------
## Quickstart
_jDAS_ depends on the following Python libraries:
- [TensorFlow](https://www.tensorflow.org/) (`>= 2.2.0`): while training and inference is much faster on a GPU, the CPU version of TensorFlow is sufficient in case problems arise installing the CUDA dependencies.
- [NumPy](https://numpy.org/) and [SciPy](https://scipy.org/) for numerical manipulations.
- [Matplotlib](https://matplotlib.org/) for visualisation.
- [h5py](https://www.h5py.org/) for IO.
- (Optional) [Jupyter](https://jupyter.org/) notebook or lab to run the examples
All of these dependencies can be installed with [Anaconda](https://www.anaconda.com/products/individual):
```
conda install -c conda-forge numpy scipy matplotlib h5py "tensorflow-gpu>=2.2.0"
```
To obtain the _jDAS_ source code, you can pull it directly from the GitHub repository:
```
git clone https://github.com/martijnende/jDAS.git
```
No additional building is required. To test the installation, try running one of the examples Jupyter notebooks in the `examples` directory.
Please open a ticket under the tab "Issues" on the GitHub repository if you have trouble setting-up _jDAS_.
--------------
## Explainer video
--------------
## Citing _jDAS_
For use of _jDAS_ in scientific applications, please consider citing the following publication:
```
@article{vandenEnde2021,
author={van den Ende, Martijn Peter Anton and Lior, Itzhak and Ampuero, Jean-Paul and Sladen, Anthony and Ferrari, André and Richard, Cédric},
title={A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data},
publisher={IEEE Transactions on Neural Networks and Learning Systems}, doi={10.1109/TNNLS.2021.3132832}, year={2021}, volume={0}
}
```
An identical preprint is available from EarthArxiv: https://eartharxiv.org/repository/view/2136/.