加拿大阿尔伯塔泥盆纪沉积卤水中非常规锂资源的数据驱动勘探方法

IF 4.8 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Xiaolong Peng, Zhuoheng Chen, Chunqing Jiang, Wanju Yuan, Jiangyuan Yao
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引用次数: 0

摘要

随着全球市场的蓬勃发展、直接提取技术的进步以及与传统采矿方法相比对环境的影响更小,富锂(Li-rich)沉积盐水已成为一种宝贵的非常规资源。然而,由于现场采样效率低下,以及Li浓度([Li])与环境敏感的地球化学指标之间的相关性不可靠,资源圈定和估算仍然具有挑战性。在公开数据和新近获得的阿尔伯塔泥盆纪盐水水化学测量数据的支持下,我们开发了一种基于截止值的数据驱动方法,在概率域中提取富含锂的环境特征,以预测有水化学数据但没有[Li]测量的地点的[Li]水平。该方法仅依赖于常用的地理空间(坐标、地层位置)和地球化学特征,包括总溶解固体(TDS)和Na、K、Mg和Ca阳离子的含量。通过对2022年5月以后测量的大约100个Li标记样品进行验证,该方法在预测三个[Li]截止水平(即>; 35 Mg /L、>; 50 Mg /L和>; 75 Mg /L)方面分别达到了97%和84%的最低精度和准确度。随后,该方法利用遗留的水化学数据预测了897个不同地点的地层水[Li]水平。结果与观测到的阿尔伯塔泥盆纪富锂盐水趋势在空间上保持一致,并将资源圈定和估计能力扩展到[Li]数据可用性有限的地区和地层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Data-Driven Approach for Exploring Unconventional Lithium Resources in Devonian Sedimentary Brines, Alberta, Canada

Lithium-rich (Li-rich) sedimentary brine has emerged as a valuable unconventional resource, driven by the blooming global market, advancements in direct extraction technologies, and a lower environmental impact compared to traditional mining methods. However, resource delineation and estimation remain challenging due to inefficient field sampling and unreliable correlations between Li concentration ([Li]) and environment-sensitive geochemical indicators. Supported by public data and newly acquired measurements of water chemistry for Alberta Devonian brines, we developed a cutoff-based data-driven approach to extract Li-rich environmental characteristics in the probability domain to predict [Li] levels at locations with water chemistry data but without [Li] measurements. The approach relies solely on commonly available geospatial (coordinates, stratigraphic position) and geochemical features, including contents of total dissolved solids (TDS) and cations of Na, K, Mg, and Ca. Validated against about one hundred Li-labeled samples measured after May 2022, the approach achieved a minimum precision and accuracy of 97% and 84%, respectively, for predicting three [Li] cutoff levels (i.e., > 35 mg/L, > 50 mg/L, and > 75 mg/L). It was subsequently applied to predict [Li] levels of formation water from 897 different locations with legacy water chemistry data. The results align spatially with observed trends of Li-rich brines in Alberta Devonian formations and expand resource delineation and estimation capabilities to areas and formations with limited [Li] data availability.

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来源期刊
Natural Resources Research
Natural Resources Research Environmental Science-General Environmental Science
CiteScore
11.90
自引率
11.10%
发文量
151
期刊介绍: This journal publishes quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Typical articles use geoscientific data or analyses to assess, test, or compare resource-related aspects. NRR covers a wide variety of resources including minerals, coal, hydrocarbon, geothermal, water, and vegetation. Case studies are welcome.
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