限制了多种气候，岩性,地形,控制大河同位素变化的地球化学变量通常是标准统计方法的挑战。机器学习(ML)是一种分析多维数据集的有效方法，解决相关的过程，同时探索变量之间的关系。我们测试了四种ML算法，以阐明育空河流域（YRB）上河流δ7Li变化的控制。我们编制了(n=102)并分析了新样本(n=21)，生成夏季在整个流域收集的123个河水样本的数据集，包括δ7Li和提取的环境，气候学,以及来自开放获取地理空间数据库的每个样本的流域地质特征。对ML模型进行了训练，调谐，并在多个场景下进行测试，以避免过度拟合等问题。随机森林(RF)在预测整个盆地的δ7Li时表现最好，中位数模型解释了62%的方差。控制整个盆地δ7Li的最重要变量是海拔，岩性,和过去的冰川覆盖，最终影响风化一致性。河流δ7Li对海拔具有负依赖性。这反映了动力学受限山区中停留时间短的全等风化。岩性的一致排序，特别是火成岩和变质岩覆盖层，作为由RFs建模的控制河流δ7Li的顶级特征是出乎意料的。需要进一步的研究来验证这一发现。由于不成熟的风化剖面导致停留时间短，在最后一次冰川最大值期间被广泛覆盖的河流排水区域往往具有较低的δ7Li。次生矿物形成较少，因此风化较一致。我们证明了ML提供了一种快速的，简单,可可视化和可解释的方法，用于解开河水同位素变化的关键控制。我们认为ML应该成为一种常规工具，并提出了应用ML分析流域尺度空间金属同位素数据的框架。
Constraining the multiple climatic, lithological, topographic, and geochemical variables controlling isotope variations in large rivers is often challenging with standard statistical methods. Machine learning (ML) is an efficient method for analyzing multidimensional datasets, resolving correlated processes, and exploring relationships between variables simultaneously. We tested four ML algorithms to elucidate the controls of riverine δ7Li variations across the Yukon River Basin (YRB). We compiled (n = 102) and analyzed new samples (n = 21), producing a dataset of 123 river water samples collected across the basin during the summer including δ7Li and extracted environmental, climatological, and geological characteristics of the drainage area for each sample from open-access geospatial databases. The ML models were trained, tuned, and tested under multiple scenarios to avoid issues such as overfitting. Random Forests (RF) performed best at predicting δ7Li across the basin, with the median model explaining 62 % of the variance. The most important variables controlling δ7Li across the basin are elevation, lithology, and past glacial coverage, which ultimately influence weathering congruence. Riverine δ7Li has a negative dependence on elevation. This reflects congruent weathering in kinetically-limited mountain zones with short residence times. The consistent ranking of lithology, specifically igneous and metamorphic rock cover, as a top feature controlling riverine δ7Li modeled by the RFs is unexpected. Further study is required to validate this finding. Rivers draining areas that were extensively covered during the last glacial maximum tend to have lower δ7Li due to immature weathering profiles resulting in short residence times, less secondary mineral formation and therefore more congruent weathering. We demonstrate that ML provides a fast, simple, visualizable, and interpretable approach for disentangling key controls of isotope variations in river water. We assert that ML should become a routine tool, and present a framework for applying ML to analyze spatial metal isotope data at the catchment scale.
