Supervised data-driven mineral prospectivity mapping (MPM) usually employs both positive and negative training datasets. Positive training datasets are typically created using the locations of known mineral deposits, whereas negative training datasets can be generated using the locations of random points. However, not all the negative points can be treated as true negative samples, which means that the selection of random negative training points creates uncertainty. This study provides a framework for addressing the effects of random negative training points on MPM. Fifty negative training datasets were generated using random point locations, and 50 mineral potential maps were created using logistic regression model. The area under the receiver operating characteristic curves (AUC) was used to evaluate the MPM performance. The mean of the AUC was employed to represent the average spatial correlation between mineralization and the selected spatial patterns, and the standard deviation of the AUC was used to indicate uncertainty relating to the use of random negative training samples. Additionally, a risk and return analysis was conducted to explore the uncertainty of the mineral potential map due to the use of random negative samples, and an odds ratio of probability was employed to describe the chances of both the occurrence and non-occurrence of a mineral deposit. The risk and return maps were obtained using the average and standard deviation of the log odds ratio per location. The mean of the log odds ratio was transformed into a mean probability, which can be regarded as the final mineral potential value that considers uncertainty due to random negative samples. Initial mineral exploration can thus be prioritized in areas that have low risk and high return compared to other areas. The proposed framework was demonstrated by mapping potential for skarn Fe mineralization in southwestern Fujian, China.

监督数据驱动的矿产前景勘测图（MPM）通常采用正面和负面的训练数据集。正训练数据集通常使用已知矿床的位置创建，而负训练数据集可以使用随机点的位置生成。但是，并非所有的负点都可以视为真实的负样本，这意味着选择随机的负训练点会产生不确定性。这项研究提供了一个框架，以解决随机负面训练点对MPM的影响。使用随机点位置生成了五十个负面训练数据集，并使用逻辑回归模型创建了50个矿物质潜力图。接收器工作特性曲线（AUC）下的面积用于评估MPM性能。AUC的平均值用于表示矿化与所选空间模式之间的平均空间相关性，AUC的标准偏差用于指示与使用随机负训练样本有关的不确定性。此外，进行了风险和收益分析，以探讨由于使用随机负样本而导致的矿产潜力图的不确定性，并使用概率的比值比来描述矿床发生和未发生的机会。的风险和回报使用每位置中的日志比值比的平均值和标准偏差，获得地图。对数比值比的平均值转换为平均概率，可以将其视为考虑了随机负样本所导致的不确定性的最终矿产潜力值。因此，可以在低风险和高回报的地区优先进行初始矿产勘探与其他地区相比。通过绘制福建西南部矽卡岩型铁矿化潜力的例证，证明了拟议的框架。