The future of AI in critical mineral exploration

TL;DR

This paper proposes an AI-driven scientific method for critical mineral exploration, emphasizing Bayesian principles and falsification to reduce bias, improve decision-making, and enhance discovery success.

Contribution

It introduces a novel scientific framework combining Bayesian falsification with AI techniques, including unsupervised learning and human-in-the-loop algorithms, for more effective mineral exploration.

Findings

New protocol for data acquisition in exploration campaigns

Development of unsupervised learning methods for hypothesis generation

Human-in-the-loop AI algorithms for optimal data planning

Abstract

The energy transition through increased electrification has put the worlds attention on critical mineral exploration Even with increased investments a decrease in new discoveries has taken place over the last two decades Here I propose a solution to this problem where AI is implemented as the enabler of a rigorous scientific method for mineral exploration that aims to reduce cognitive bias and false positives drive down the cost of exploration I propose a new scientific method that is based on a philosophical approach founded on the principles of Bayesianism and falsification In this approach data acquisition is in the first place seen as a means to falsify human generated hypothesis Decision of what data to acquire next is quantified with verifiable metrics and based on rational decision making A practical protocol is provided that can be used as a template in any exploration campaign However in order to make this protocol practical various form of artificial intelligence are needed I will argue that the most important form are one novel unsupervised learning methods that collaborate with domain experts to better understand data and generate multiple competing geological hypotheses and two humanintheloop AI algorithms that can optimally plan various geological geophysical geochemical and drilling data acquisition where uncertainty reduction of geological hypothesis precedes the uncertainty reduction on grade and tonnage