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| 1 | Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions显示文摘The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has examined many machine learning and modelling techniques that combine exploration criteria,or ’features’, to generate predictions for mineral prospectivity. Central to the design of prospectivity models is a ’mineral system’, a conceptual model describing the key geological elements that control the timing and location of economic mineralisation. The mineral systems model defines what constitutes a training set, which features represent geological evidence of mineralisation, how features are engineered and what modelling methods are used. Mineral systems are knowledge-driven conceptual models, thus all parameter choices are subject to human biases and opinion so alternative models are possible.However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested.(1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model.(2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison.(3)Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units.(4) The training dataset is composed of known economic mineralisation sites(deposits) as ’positive’ examples, and exploration drilling data providing ’negative’sampling locations. Each of the spatial predictions are assessed using independent performance metrics common to AI-based classification methods and subjected to geological plausibility testing. We find that different conceptual mineral systems produce significantly different spatial predictions, thus conceptual uncertainty must be recognised. A benefit to recognising and modelling different conceptual models is that robust and geologically plausible predictions can be made that may guide mineral discovery. | Mark D Lindsay Agnieszka M.Piechocka Mark W Jessell Richard Scalzo Jeremie Giraud Guillaume Pirot Edward Cripps | 2022 | Geoscience Frontiers2022，13，6： | 1 |
| 2 | 基于KBRF算法的镍基690合金应力腐蚀裂纹扩展速率预测模型显示文摘镍基690合金广泛用于压水堆核电站核岛主设备关键部件及焊缝,高温高压水环境应力腐蚀开裂(SCC)是其潜在的失效机理。由于SCC行为影响因素多达二十余种,因此存在参数化模型预测精度不高的问题。本研究通过融合随机森林机器学习算法(random forest,RF)与基于领域知识的MRP-386参数化模型,建立了镍基690合金SCC裂纹扩展速率KBRF(knowledge-based random forest)预测模型。结果表明,领域知识的引入增强了KBRF模型的鲁棒性,准确性较MRP-386参数化模型和RF等机器学习模型显著提高。模型将应用于中国压水堆核电站镍基690合金部件及焊缝在反应堆冷却剂中的应力腐蚀裂纹扩展工程预测。 | 梅金娜 王鹏 韩姚磊 蔡振 遆文新 彭群家 薛飞 | 2022 | 稀有金属材料与工程2022，51，4： | 0 |
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