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Data-driven classification of bedrocks by the measured uranium content using self-organizing maps
Applied Geochemistry ( IF 3.4 ) Pub Date : 2021-08-12 , DOI: 10.1016/j.apgeochem.2021.105074
Ying Wang 1 , Marco Brönner 1 , Vikas Chand Baranwal 1 , Hendrik Paasche 1, 2 , Alexandros Stampolidis 1, 3
Affiliation  

Uranium is a naturally occurring element that can be found almost everywhere in rocks and soils throughout the earth's crust. One of its decay products, radon, is of gaining concern in recent years because this colourless, odourless, tasteless gas is proven to be responsible for many lung cancer cases each year. Analysing the spatial distribution of the uranium concentration in the ground surface can help predict radon hazard regions. In this study, two types of uranium measurements – airborne gamma-ray spectrometry (AGRS) and ground-based rock sample analysis via inductively coupled plasma mass spectrometry (ICP-MS) – are calibrated for the purpose. The two types of data with different sampling schemes are found to have a reasonable correlation to each other when using the mapped geology as categorical units. This finding confirms the feasibility of using geological maps as a first-order predictor to map uranium, and further radon, in a larger scale. We then apply the self-organizing maps (SOM) technique for a data-driven classification of rock types based on the measured uranium content. The presented study area is located at mid-Norway in the Trøndelag county, the same study will be performed in other regions across Norway where both types of measurements are in abundance.

This study contributes to an on-going project to map radon hazard zones throughout Norway. While the radon hazard is defined by the indoor radon level which is affected by two folds of factors – geogenic (uranium-rich subsurface) and anthropogenic (dwelling type, indoor air exchange, etc.), our work aims to single out the geogenic factor. Comparing to the current national Radon Awareness Map of Norway (URL: http://geo.ngu.no/kart/radon/), where bedrocks were categorized by their likelihood of hosting elevated indoor radon, our approach utilizes measured uranium concentration of the ground which has a more direct link to the bedrock types.



中文翻译:

使用自组织图通过测量的铀含量对基岩进行数据驱动分类

铀是一种天然存在的元素,几乎可以在整个地壳的岩石和土壤中找到。氡是其衰变产物之一,近年来备受关注,因为这种无色、无味、无味的气体已被证明是每年许多肺癌病例的罪魁祸首。分析地表中铀浓度的空间分布有助于预测氡危害区域。在这项研究中,两种类型的铀测量 - 机载伽马射线光谱法 (AGRS) 和通过电感耦合等离子体质谱法 (ICP-MS) 进行的地面岩石样品分析 - 为此目的进行了校准。当使用映射地质作为分类单位时,发现具有不同采样方案的两类数据彼此具有合理的相关性。这一发现证实了使用地质图作为一级预测器来绘制更大比例的铀和氡的可行性。然后,我们将自组织图 (SOM) 技术应用于基于测量的铀含量的岩石类型的数据驱动分类。所提出的研究区域位于挪威中部的特伦德拉格县,同样的研究将在挪威的其他地区进行,那里两种类型的测量都很丰富。

这项研究有助于绘制整个挪威的氡危险区域的正在进行的项目。虽然氡危害是由受双重因素影响的室内氡水平定义的 - 地质(富含铀的地下)和人为(居住类型、室内空气交换等),我们的工作旨在挑出地质因素. 与挪威当前的国家氡意识地图(网址:http://geo.ngu.no/kart/radon/)相比,基岩根据其承载室内氡的可能性进行分类,我们的方法利用测量的铀浓度与基岩类型有更直接联系的地面。

更新日期:2021-08-15
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