Optimal Bayesian decision making on what geoscientific data to acquire requires stating a prior model of uncertainty. Data acquisition is then optimized by reducing uncertainty on some property of interest maximally, and on average. In the context of exploration, very few, sometimes no data at all, is available prior to data acquisition planning. The prior model therefore needs to include human interpretations on the nature of spatial variability, or on analogue data deemed relevant for the area being explored. In mineral exploration, for example, humans may rely on conceptual models on the genesis of the mineralization to define multiple hypotheses, each representing a specific spatial variability of mineralization. More often than not, after the data is acquired, all of the stated hypotheses may be proven incorrect, i.e. falsified, hence prior hypotheses need to be revised, or additional hypotheses generated. Planning data acquisition under wrong geological priors is likely to be inefficient since the estimated uncertainty on the target property is incorrect, hence uncertainty may not be reduced at all. In this paper, we develop an intelligent agent based on partially observable Markov decision processes that plans optimally in the case of multiple geological or geoscientific hypotheses on the nature of spatial variability. Additionally, the artificial intelligence is equipped with a method that allows detecting, early on, whether the human stated hypotheses are incorrect, thereby saving considerable expense in data acquisition. Our approach is tested on a sediment-hosted copper deposit, and the algorithm presented has aided in the characterization of an ultra high-grade deposit in Zambia in 2023.

翻译：关于获取何种地球科学数据的最优贝叶斯决策制定，需要先陈述一个不确定性先验模型。随后通过最大程度地、平均地减少目标属性的不确定性来优化数据采集。在勘探背景下，进行数据采集规划之前，可用的数据极少，有时甚至完全没有数据。因此，先验模型需要纳入人类对空间变异性质的解释，或被视为与勘探区域相关的类比数据。例如，在矿产勘探中，人类可能依赖关于矿化成因的概念模型来定义多种假设，每种假设代表一种特定的矿化空间变异性。然而，在数据采集之后，所有已陈述的假设很可能都被证明是不正确的，即被证伪，因此需要修正先验假设或生成新的假设。在错误的地质先验下规划数据采集很可能是低效的，因为对目标属性的不确定性估计是不正确的，从而可能完全无法降低不确定性。本文开发了一种基于部分可观测马尔可夫决策过程的智能体，该智能体在存在多种关于空间变异性质的地质或地球科学假设的情况下进行最优规划。此外，该人工智能配备了一种方法，能够早期检测人类陈述的假设是否错误，从而节省大量的数据采集成本。我们的方法在一个沉积岩型铜矿床案例中进行了测试，所提出的算法已于2023年协助完成了赞比亚一个超高品位矿床的特征描述。