Modern geological studies and mineral exploration techniques rely heavily on being able to digitally visualise and interpret data. Rare earth elements (REEs) are vital for renewable energy technologies. REE concentrations, when normalised to a standard material, show unique geometric curves (or patterns) in geological samples due to their similar chemical properties. The lambda technique can be used to describe these patterns and turn them into points - making it easier to visualise and interpret larger datasets. Lambdas have the potential to help industry understand intricate sample relationships and the geological and economic importance of their data. This study explored the use of lambdas through the evaluation of various visualisation methods to determine their usefulness in mineral exploration. The 'REEkit' platform facilitated the evaluation of the different visualisation methods and gauged industry interest and acceptance of such a service. Qualitative data was gathered through contextual inquiry, utilising semi-structured interviews and an observational session with 10 participants. Conceptual thematic analysis was applied to extract key findings. This study found that two critical factors for successful lambda data visualisation in the mineral exploration industry are familiarity and clarity: visualisations that were familiar and commonplace for users allowed for better analysis and clear communication to non-technical audiences. This included visualisations such as the 3D scatter plot and scatter plot matrix. Furthermore, visualisations that complemented each other and seamlessly integrated into the same workflow provided diverse perspectives on the data. Important aspects included understanding population grouping versus data distribution, achieved through combinations such as scatter plot and density contour plot, or 3D scatter plot and violin plot.

翻译：现代地质研究和矿产勘探技术高度依赖于对数据的数字化可视化与解读能力。稀土元素对于可再生能源技术至关重要。当稀土元素浓度归一化至标准物质后，由于它们相似的化学性质，在地质样本中会呈现出独特的几何曲线（或模式）。λ技术可用于描述这些模式并将其转化为数据点——从而更便于对大规模数据集进行可视化与解读。λ值在帮助工业界理解复杂样本关系、以及数据蕴含的地质与经济意义方面具有潜力。本研究通过评估多种可视化方法，探索了λ值的应用，以确定其在矿产勘探中的实用性。'REEkit'平台促进了不同可视化方法的评估，并衡量了工业界对此类服务的兴趣与接受度。通过情境调查（采用半结构化访谈与十名参与者的观察环节）收集了定性数据，并应用概念主题分析提取关键发现。本研究发现，在矿产勘探行业中，实现λ数据可视化成功的关键因素为熟悉度与清晰度：用户熟悉且常见的可视化方式（如三维散点图与散点图矩阵）更有利于分析，并能清晰地向非技术受众传达信息。此外，能够相互补充并无缝集成至同一工作流的可视化方法（如散点图与密度等值线图的组合，或三维散点图与提琴图的组合），为数据提供了多元视角。重要方面包括通过此类组合实现群体分组与数据分布的理解。