This paper demonstrates knowledge-guided fuzzy logic modeling of regional-scale surficial uranium (U) prospectivity in British Columbia (Canada). The deposits/occurrences of surficial U in this region vary from those in Western Australia and Namibia; thus, requiring innovative and carefully-thought techniques of spatial evidence generation and integration. As novelty, this papers introduces a new weighted fuzzy algebraic sum operator to combine certain spatial evidence layers. The analysis trialed several layers of spatial evidence based on conceptual mineral system model of surficial U in British Columbia (Canada) as well as tested various models of evidence integration. Non-linear weighted functions of (a) spatial closeness to U-enriched felsic igneous rocks was employed as U-source spatial evidence, (b) spatial closeness to paleochannels as fluid pathways spatial evidence, and (c) surface water U content as chemical trap spatial evidence. The best models of prospectivity created by integrating the layers of spatial evidence for U-source, pathways and traps predicted at least 85% of the known surficial U deposits/occurrences in >10% of the study region with the highest prospectivity fuzzy scores. The results of analyses demonstrate that, employing the known deposits/occurrences of surficial U for scrutinizing the spatial evidence layers and the final models of prospectivity can pinpoint the most suitable critical processes and models of data integration to reduce bias in the analysis of mineral prospectivity.

本文演示了不列颠哥伦比亚（加拿大）的区域规模表层铀（U）前景的知识指导模糊逻辑建模。该地区表层铀的沉积/赋存与西澳大利亚州和纳米比亚的不同。因此，需要创新和经过深思熟虑的空间证据生成和整合技术。作为新颖性，本文介绍了一种新的加权模糊代数和运算符，用于组合某些空间证据层。该分析基于不列颠哥伦比亚（加拿大）的表层铀的概念性矿物系统模型，对多层空间证据进行了试验，并测试了各种证据整合模型。（a）与富铀长英质火成岩在空间上接近的非线性加权函数被用作铀源的空间证据，（b）到古河道的空间接近性是流体通道的空间证据，（c）地表水U含量是化学陷阱的空间证据。通过整合U源，路径和陷阱的空间证据层而创建的最佳前瞻性模型预测，在> 10％的研究区域中，至少有85％的已知表面U沉积物/赋存量具有最高的前瞻性模糊评分。分析结果表明，利用已知的表层铀矿床/成因，仔细检查空间证据层，最终的预期模型可以指出最合适的关键过程和数据整合模型，以减少矿物预期分析中的偏差。通过整合U源，路径和陷阱的空间证据层而创建的最佳前瞻性模型预测，在> 10％的研究区域中，至少有85％的已知表面U沉积物/赋存量具有最高的前瞻性模糊评分。分析结果表明，利用已知的表层铀矿床/成因，仔细检查空间证据层，最终的预期模型可以指出最合适的关键过程和数据整合模型，以减少矿物预期分析中的偏差。通过整合U源，路径和陷阱的空间证据层而创建的最佳前瞻性模型预测，在> 10％的研究区域中，至少有85％的已知表面U沉积物/赋存量具有最高的前瞻性模糊评分。分析结果表明，利用已知的表层铀矿床/成因，仔细检查空间证据层，最终的预期模型可以指出最合适的关键过程和数据整合模型，以减少矿物预期分析中的偏差。