用AI重构找矿科学方法，减少偏见与误判。
The future of AI in critical mineral exploration
- 基于贝叶斯与可证伪性哲学，以数据驱动假说验证
- 提出可量化决策的勘探流程，降低认知偏差与成本
- 融合无监督学习与人机协作算法，优化多源数据采集
能源转型推动电气化，使关键矿物勘探备受关注。尽管投资增加，过去二十年新发现却呈下降趋势。本文提出一种以人工智能为引擎的严谨科学方法，旨在减少认知偏见和误报，降低勘探成本。该方法基于贝叶斯主义与可证伪性哲学，将数据获取视为验证人类假说的手段。下一步数据采集决策通过可验证指标量化，依托理性判断。提供一套可复用的实践协议，适用于任何勘探项目。为实现该协议，需多种人工智能技术支撑：一是新型无监督学习方法，协助领域专家理解数据并生成多个竞争性地质假说；二是人机协同的AI算法，可最优规划地质、地球物理、地球化学及钻探数据采集，优先减少地质假说的不确定性，再聚焦品位与储量不确定性。
原文摘要 · Abstract (English)
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
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