DeepWave-KAUST/DLDAS_Denoising-pub
Official reproducible material for Noise attenuation in distributed acoustic sensing data using a guided unsupervised deep learning network
This project helps geophysicists and seismic data analysts improve the quality of Distributed Acoustic Sensing (DAS) data. It takes raw DAS measurements, which are often contaminated with unwanted noise, and processes them to produce cleaner, denoised acoustic data. This tool is designed for professionals working with seismic surveys and subsurface imaging who need to extract reliable information from noisy DAS recordings.
No commits in the last 6 months.
Use this if you are working with Distributed Acoustic Sensing (DAS) data and need to effectively remove noise to get clearer seismic signals for analysis.
Not ideal if your primary data source is not Distributed Acoustic Sensing or if you require real-time processing rather than batch denoising.
Stars
18
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 04, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/DeepWave-KAUST/DLDAS_Denoising-pub"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
CAREamics/careamics
A deep-learning library for denoising images using Noise2Void and friends (CARE, PN2V, HDN...
yu4u/noise2noise
An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration...
rgeirhos/texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased...
NICALab/SUPPORT
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
jaewon-lee-b/lte
Local Texture Estimator for Implicit Representation Function, in CVPR 2022