This repository contains the material used by the SeismicHunt project in the First KAUST Hackathon in Geoscience!

The Earth is filled with an invaluable amount of resources that power our daily lives. Natural gas, for example, is going to play a key role in the energy transition as its use is required to produce blue hydrogen. Nevertheless finding such resources is hard and geoscientists play a constant treasure hunt when interpreting seismic data. We wish therefore to devise an artificially intelligent tool that provided with a large set of unlabeled seismic pre-stack data and a small labeled dataset can detect residual gas accumulation in the subsurface (and possibly their shape and saturation).

The following notebooks are provided:

SeismicHunt_gettingstarted: display the training and testing datasets (requires data to be stored locally;

SeismicHunt_gettingstarted_colab: same but in case you are using Colab;

SeismicHunt: data for KAUST ML Hackathon 2022

When working on your local machine, we suggest to have installed Anaconda or Miniconda on your computer. If you are not familiar with it, we suggesting using the KAUST Miniconda Install recipe. This has been tested both on macOS and Unix operative systems. Once this is installed, create a new environment using the environment.yml file proved here by simply typing on terminal:

conda env create -f environment.yml

Note that this file is meant to be a file for template environment, feel free to include other libraries that you will be using!