Tutorial: Self-supervised noise suppression
Channel: Software Underground
Date: 2022-04-27

Claire Birnie & Sixiu Liu

What you'll need:
- Slack channel: #t22-wed-noise-suppression (visit https://softwareunderground.org/slack to join)
- Repo: https://github.com/swag-kaust/Transform2022_SelfSupervisedDenoising
- Pre-requisites: Experience with deep learning and python

Self-supervised learning offers a solution to the common limitation of the lack of noisy-clean pairs of data for training deep learning seismic denoising procedures.

In this tutorial, we will explain the theory behind blind-spot networks and how these can be used in a self-supervised manner, removing any requirement of clean-noisy training data pairs. We will deep dive into how the original methodologies for random noise can be adapted to handle realistic noise in seismic data, both pseudo-random noise and structured noise. Furthermore, each sub-topic presented will be followed by a live, code-along session such that all participants will be able to recreate the work shown and can afterwards apply it to their own use cases.

TIMESTAMPS
00:00:00 Start streaming
00:00:08 Transform 2022 Information
00:01:30 Instructors and Schedule

Presentation. Intro to N2V (Noise to Void)
06:04 Part One - Random noise suppression
07:01 Silence
07:28 Fixed it
11:08 Recap Different Deep learning denoising procedures
11:54 Self-supervised learning examples
13:51 Noise to Void Methodology
17:13 Noise to Void Hyper-parameters

Code
18:49 Tutorial 1 -Random Noise Suppression (N2V with WGN)

Presentation. Adapting to seismic noise
47:30 Part Two - Seismic "random" noise suppression

Code
56:35 Tutorial 2 - Pseudo Random Noise Suppression

Presentation. Coherent noise suppression
1:12:10 Part Three - Coherent noise suppression

Code
1:18:00 Tutorial 3 - Trace-wise Noise Suppression

Presentation
1:36:00 Wrap-up