Abstract
MLDAS is a Python-written package for exploratory data analysis and deep learning training on Distributed Acoustic Sensing data. The machine learning tools are powered by the PyTorch library and designed to work efficiently on large scale datasets using parallel computing. Various SLURM scripts as well as a tutorial have also been made available to allow geophysicists to quickly and easily implement the available tools in their analysis workflow on supercomputer facilities.
- Developers:
-
Dumont, Vincent [1]
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Release Date:
- 2020-10-21
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231
- Code ID:
- 51877
- Site Accession Number:
- 2020-130
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Dumont, Vincent.
Machine Learning for Distributed Acoustic Sensing data (MLDAS) v1.0.1.
Computer Software.
https://gitlab.com/ml4science/mldas.
USDOE.
21 Oct. 2020.
Web.
doi:10.11578/dc.20210302.1.
Dumont, Vincent.
(2020, October 21).
Machine Learning for Distributed Acoustic Sensing data (MLDAS) v1.0.1.
[Computer software].
https://gitlab.com/ml4science/mldas.
https://doi.org/10.11578/dc.20210302.1.
Dumont, Vincent.
"Machine Learning for Distributed Acoustic Sensing data (MLDAS) v1.0.1." Computer software.
October 21, 2020.
https://gitlab.com/ml4science/mldas.
https://doi.org/10.11578/dc.20210302.1.
@misc{
doecode_51877,
title = {Machine Learning for Distributed Acoustic Sensing data (MLDAS) v1.0.1},
author = {Dumont, Vincent},
abstractNote = {MLDAS is a Python-written package for exploratory data analysis and deep learning training on Distributed Acoustic Sensing data. The machine learning tools are powered by the PyTorch library and designed to work efficiently on large scale datasets using parallel computing. Various SLURM scripts as well as a tutorial have also been made available to allow geophysicists to quickly and easily implement the available tools in their analysis workflow on supercomputer facilities.},
doi = {10.11578/dc.20210302.1},
url = {https://doi.org/10.11578/dc.20210302.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20210302.1}},
year = {2020},
month = {oct}
}