Lithofacies Classification by Implementing Machine and Deep Learning Interactive Dashboard - FORCE Dataset

The project is named 'Machine and Deep learning for lithofacies classification in the North Sea', which aimed to develop several machine and deep learning models to classify lithofacies in the North Sea. The project was supervised by Arild Buland, UiS adjunct professor and Leader in geophysical methods and anlysis at Equinor.

Find the complete repository holding the experimentation, execution, models, utility functions. (https://github.com/JohnMasapantaPozo/Machine-and-Deep-Learning-Applied-to-Geosciences)

Creator

• John Masapanta Pozo - JohnMasapantaPozo

Getting started

Users:

The application was created, deployed, and hosted by Streamlit. If you want to try the web application follow the link. .

Developers:

• If you want to play with the code you could clone the whole repository to your local machine by:

  • git clone https://github.com/JohnMasapantaPozo/Litho-Machine-Leraning-Web-App.git

• Once you have cloned the repository, you will need to reproduce the environment needed by the application. Open your command prompt CMD and recreate the environment by using the 'environment.yml' file:

  • 'conda env create -f environment.yml'

• Once everything is set you could interact with the code locally and run the app locally by:

  • 'streamlit run main.py'