Lessons learnt for tuning a Machine Learning fault prediction model
We have an article in the First Break magazine from the EAGE (European Association of Geoscientists and Engineers) this month.

In this article my colleagues Hadyan Pratama, Matthew Oke, Wayne Mogg, David M., Arnaud Huck and Paul de Groot describe a series of experiments they did to improve fault likelihood predictions of a pre-trained Machine Learning model on an unseen dataset with real fault predictions. You can read the article for free by clicking on this link