The Future of Machine Learning and Model Validation in the Earth Sciences: The Good, the Bad, and the Ugly

The earth sciences are being transformed by advances in machine learning (ML) and artificial intelligence. From optimizing mineral estimation and hydrocarbon production to improving wildfire prediction and management, these methods offer exciting opportunities for modeling and decision-making. However, these advances bring challenges with model validation, which is critical for ensuring that predictions are robust, reasonable, and actionable.
This lecture will delve into the evolving role of ML in the mining, hydrocarbon, or wildfire industry, highlighting successes, pitfalls, and future prospects. "The Good" will explore case studies and implementations where ML has significantly improved modeling, decision making, and inference. "The Bad" will examine common pitfalls, including data biases, overfitting, and the misuse of algorithms without understanding domain constraints. Finally, "The Ugly" will confront the ethical and operational risks posed by poorly validated models, emphasizing the importance of transparency and domain experts.
This lecture will not only focus on ML methods, but will also consider how to validate all types of earth science models including estimates, simulations, and decision making. We will discuss best practices for integrating ML models into traditional workflows while addressing the complexities of model validation.