Previously, in our series on OpendTect Machine Learning workflows, we showed an unsupervised workflow for seismic facies analysis. That workflow clustered seismic waveforms to generate a segmentation volume consisting of 50 different segments enabling detailed interpretation of seismic facies.
Today, we show one of several possible workflows in OpendTect using supervised learning. This workflow uses the Thalweg tracker for labeling target positions. In total 8 different label sets were created representing positive and negative amplitude classes of meandering channels, unconfined channels, splays and floodplains.
See blog post on dgbes.com
See blog post on LinkedIn
Duration: 0:55