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首页> 外文期刊>Computers & geosciences >Regression trees for modeling geochemical data-An application to Late Jurassic carbonates (Ammonitico Rosso)
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Regression trees for modeling geochemical data-An application to Late Jurassic carbonates (Ammonitico Rosso)

机译：用于地球化学数据建模的回归树-在晚侏罗世碳酸盐岩中的应用（Ammonitico Rosso）

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Research based on ancient carbonate geochemical records is often assisted by multivariate statistical analysis, among others, used for data mining. This contribution reports a complementary approach that can be applied to paleoenvironmental research. The choice to use a machine learning method, here regression trees (RT), relied in the ability to learn complex patterns, integrating multiple types of data with different statistical distributions to obtain a knowledge model of geochemical behavior along a paleo-platform. The Late Jurassic epioceanic deposits under scope are represented by six stratigraphic sections located in SE Spain and on the Majorca Island. The used database comprises a total of 1960 data points corresponding to eight variables (stable C and O isotopes, the elements Ca, Mg, Sr, Fe, Mn and skeletal content). This study uses RT models in which the predictive variables are the geochemical proxies, whilst skeletal content is used as a target variable. The resulting model is data driven, explaining variations in the target variable and providing additional information on the relative importance of each variable to each prediction, as well as its corresponding threshold values. The obtained RT revealed a structured distribution of samples, organized either by stratigraphic section or sets of nearby sections. Averaged estimated skeletal abundance confirmed the initial observations of higher skeletal content for the most distal sections with estimated values from 18% to 27%. In contrast, lower skeletal abundance from 5% to 15% is proposed for the remaining sections. The geochemical variable that best discriminates this major trend is δ~(18)O, at a threshold value of -0.2‰, interpreted as evidence for separation of water-mass properties across the studied areas. Other four variables were considered relevant by the obtained decision tree: C isotopes, Ca, Sr and Mn, providing new insights for further differentiation between sets of samples.
机译：基于古代碳酸盐地球化学记录的研究通常由多变量统计分析以及其他一些用于数据挖掘的数据辅助。这一贡献报告了一种可以应用于古环境研究的补充方法。选择使用机器学习方法（此处为回归树（RT））的能力在于学习复杂模式，整合具有不同统计分布的多种类型的数据以获取沿古平台的地球化学行为的知识模型的能力。范围内的晚侏罗世海相沉积由位于西班牙东南部和马略卡岛上的六个地层剖面所代表。使用的数据库总共包含1960个数据点，它们对应于八个变量（稳定的C和O同位素，元素Ca，Mg，Sr，Fe，Mn和骨骼含量）。本研究使用RT模型，其中预测变量是地球化学代理，而骨骼含量用作目标变量。所得模型由数据驱动，解释目标变量的变化并提供有关每个变量对每个预测的相对重要性及其相应阈值的附加信息。所获得的RT揭示了样品的结构化分布，按地层剖面或附近的剖面集进行组织。平均估计的骨骼丰度证实了最初观察到的最远端部分骨骼含量较高，估计值为18％至27％。相比之下，其余部分建议将骨骼丰度从5％降低到15％。最能区分这种主要趋势的地球化学变量是δ〜（18）O，其阈值为-0.2‰，这被解释为研究区域水质性质分离的证据。获得的决策树认为其他四个变量是相关的：C同位素，Ca，Sr和Mn，这为进一步区分样本集提供了新的见解。

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