Thursday 15 June 4 pm CET. Register here: https://attendee.gotowebinar.com/register/7055758961598683735
Join us for an exclusive webinar series featuring two back-to-back sessions that will provide a comprehensive overview of training a 2D CNN model architecture and seamlessly importing it into the OpendTect environment.
Read more: Free webinar on 'Machine Learning and OpendTect: Building and Training your 2D CNN Model'
Dear OpendTect Users,
We have made a new major release of our software: OpendTect 7.0.0, which is now available for installation/update.
Paul de Groot, Arnaud Huck and I will be at the EAGE in Vienna next week from 5th to 8th June - Booth 4314.
We will be sharing exciting updates about our upcoming major release: OpendTect version 7, scheduled to be released directly after the EAGE.
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.
Read more: Machine Learning Workflows - Supervised AI Seismic Facies
Thursday, May 25th at 4 pm CET
Explore the diverse approaches to handling noisy or problematic seismic data in OpendTect. From trusted attributes to cutting-edge Machine Learning techniques.
Read more: Free webinar on Approaches to Data Conditioning in OpendTect
Exciting News! dGB Earth Sciences Partners with BLIX Consultancy & GEO2 Engineering B.V. to provide expert support for Doordewind Wind Farm Zone
Read more: dGB provides expert support for Doordewind Wind Farm Zone
Today, in our series on OpendTect Machine Learning workflows, we show a workflow for rock property prediction using real wells.
This workflow has many variations. You can train on real or synthetic seismic data to predict well log properties of interest.
Read more: Machine Learning Workflows - Seismic Inversion using AI - Machine Driven Seismic Inversion Workflow
Today, in our series on Machine Learning Workflows, we go back to the origins of the OpendTect Machine Learning platform. OpendTect started life as a neural network-based seismic pattern recognition and attribute processing system. The primary goal of the original system was to create Chimney Cubes for fluid migration path interpretation. The software was used for geohazard interpretation and for de-risking hydrocarbon charge and seal problems.
The video shows a chimney interpretation study in the Gulf of Mexico by Roar Heggland of Equinor.
Read more: Machine Learning Workflows - De-risking charge and seal issues with AI - Neural Network Chimney Cube
Free webinar, Thursday 20 April 4 PM CET
Join us for an exclusive webinar as we unveil our latest innovative commercial plugin – the "Surface Segments" plugin
Read more: Surface Segments – The art of removing data to improve the signal to noise of your interpretation...
OpendTect Machine Learning supports supervised and unsupervised workflows for seismic facies analysis.
Today’s post discusses an unsupervised workflow. The 3D UVQ Waveform Segmentation workflow is the 3D variant of the Quick UVQ workflow that we discussed earlier in this series on OpendTect ML workflows. In Quick UVQ we segment (cluster) seismic waveforms (trace segments) around a mapped horizon into a user-defined number of segments. A typical number of segments in Quick UVQ is 10.
Read more: Machine Learning Workflows - Fast and Simple Seismic Facies Analysis - 3D UVQ Waveform Segmentation