.. jDAS documentation master file, created by sphinx-quickstart on Sat Aug 21 15:42:30 2021. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. image:: img/jDAS_logo.svg :height: 200 :align: center | *jDAS* in a nutshell -------------------- *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 `_), *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! 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. .. image:: img/jDAS_example.jpg 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. Contents -------- .. toctree:: :numbered: :maxdepth: 2 installation.rst inference.rst training.rst details.rst resources.rst license.rst reference.rst