Accurate prediction of mineral resources is imperative to meet the energy demands of modern society. Nonetheless, this task is often difficult due to estimation bias and limited interpretability of conventional statistical techniques and machine learning methods. To address these shortcomings, we propose a novel geospatial artificial intelligence approach, denoted as geographically neural network-weighted logistic regression, for mineral prospectivity mapping. This model integrates spatial patterns and neural networks, combined with the Shapley additive explanations theory to achieve accurate forecasts and provide explainable insight into mineralization within intricate spatial contexts. In a gold prospecting experiment conducted in Nova Scotia, our model outperformed other state-of-the-art models with a 5% to 16% increase in the area under the receiver operating characteristic curve metric. The presented framework further provided intuitive quantifications of the impact of geological factors on the gold mineralization in spatial settings. The innovative approach promotes novel phenomenon detection and exhibits robust capabilities and universality for classification problems within complex spatial scenarios.

中文翻译：

---

利用地理空间人工智能增强矿产前景测绘：地理神经网络加权逻辑回归方法

准确预测矿产资源对于满足现代社会的能源需求至关重要。尽管如此，由于传统统计技术和机器学习方法的估计偏差和可解释性有限，这项任务通常很困难。为了解决这些缺点，我们提出了一种新的地理空间人工智能方法，称为地理神经网络加权逻辑回归，用于矿产前景测绘。该模型集成了空间模式和神经网络，并与 Shapley 加法解释理论相结合，以实现准确的预测，并提供对复杂空间背景下矿化的可解释的见解。在新斯科舍省进行的金矿勘探实验中，我们的模型优于其他最先进的模型，接收者操作特征曲线指标下的面积增加了 5% 至 16%。所提出的框架进一步提供了地质因素对空间环境中金矿化影响的直观量化。这种创新方法促进了新现象的检测，并在复杂空间场景中的分类问题上表现出强大的能力和普遍性。