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This week we want to show you an example of the 4th Lundin model 'Lundin_GeoLab_SimpleDenoise". We believe this model is applicable to almost all datasets. Unlike the AJAX model, the SimpleDenoise model preserves the amplitude - frequency content. It only removes random noise.

This is the third and last post in our series on trained Machine Learning models developed by Lundin GeoLab that are released in OpendTect’s Machine Learning library. Two weeks ago, we presented SimpleHmult, a model to attenuate horizontal multiples. In last week’s post we showed an example of the application of Desmile, a model to remove migration smiles.

Last week we proudly announced the release of four stunning, pre-trained models from Lundin GeoLab in OpendTect’s Machine Learning solution. In last week’s blog post, we showed an example of one of these models: SimpleHmult, a 3D Unet model that attenuates horizontal multiples.

As of version 6.6.8, OpendTect’s library of pre-trained models boasts 4 spectaculair seismic enhancement models developed by Lundin’s GeoLab R&D group:

  • Desmile: This model removes steeply dipping noise and smile artefacts
  • SimpleHmult: Attenuates horizontal multiples
  • AJAX: Enhances the visual quality and interpretability
  • SimpleDeNoise: Removes “salt & pepper / jitter” noise.

Dear OpendTect Users,

We have made a patch release for our latest stable version: OpendTect 6.6.8, which is now available for installation/update.

With the addition of the Machine Learning Professional plug-in, OpendTect development is further extended by allowing Python as an additional development environment. Starting from within OpendTect, a Python shell, or integrated development environment (IDE), can be launched, and one with direct access to the OpendTect-Python link, libraries and data structures that are a pre-requisite to further research and development. An advantage of Python is its flexibility, and the ability to add new tools and libraries from the command line, so, even this environment can be easily customized with additional analytics libraries. Recently we have added extended support for PyTorch, on top of our already extensive Tensorflow/Keras and scikit-learn support.

Dip Steering is a commercial plugin of OpendTect Pro for computing and using dip and azimuth volumes (Steering Cubes) from seismic data. This powerful tool enables users to:

  • Apply structurally oriented filters
  • Extract mult-trace attributes along seismic reflectors
  • Compute volume curvature attributes
  • Extract horizons individually, or as a dense set (HorizonCube) using inversion-based flattening algorithms.

Geothermie Groep Nederland (GGN), dGB Earth Sciences and two suppliers of high-performance drilling services have joined their expertise to harvest low-enthalpy geothermal energy with temperatures ranging from 60 – 90 0C using LUC installations. LUC’s enable economic development of geothermal energy for domestic heating and heating of greenhouses, also from lower-quality aquifers in smaller-scale projects. The method consists of an innovative combination of project management, best-practices in well engineering, production technology and advanced reservoir management.

OpendTect supports the most advanced fault attributes available in the market through its Faults and Fractures plugin. Thinned Fault Likelihood or TFL is designed particularly for mapping and imaging faults and fractures based on their dips and strike range. The output is a probability volume with a razor-sharp image of the faults that can be used to extract fault planes.