As a mechanical engineer who has worked with many geologists - but who needs very specific inputs for his own work, one has to remember that engineering and geology is often like two different cultures with two different languages. It’s important to work to get a Rosetta Stone, so to speak, and to look at the final need for the geology data. You will need to take what is often very non-specific data for engineering and turn it into specific data. That may mean literature searches to create the Prior, taking the data as a Bayesian likelihood (and putting it in a good and useful database), and using your own best judgment to create the updated and useful engineering parameters that you’ll need for your own modeling.

As a video game developer, I would defer to the domain experts. Then, together we would grab a beer while discussing the half baked implementation of AI in various platforms.

Data augmentation: Augment your data to increase its size and diversity. Transfer learning: Leverage pre-trained models and fine-tune them on your dataset. Ensemble methods: Combine multiple models to improve their collective performance. By following these strategies, you'll be well-equipped to tackle data dilemmas and ensure precision in your modeling outcomes. Remember, data modeling is an iterative process that requires patience, persistence, and a willingness to learn from your data.