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首页> 外文期刊>Quaternary geochronology >Combining machine learning techniques, microanalyses and large geochemical datasets for tephrochronological studies in complex volcanic areas: New age constraints for the Pleistocene magmatism of central Italy
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Combining machine learning techniques, microanalyses and large geochemical datasets for tephrochronological studies in complex volcanic areas: New age constraints for the Pleistocene magmatism of central Italy

机译：复杂火山地区题头研究的机器学习技术，微肿瘤和大型地球化学数据集：意大利中部岩浆作用

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Characterization, correlation and provenance determination of tephra samples in sedimentary sections (tephrochronological studies) are powerful tools for establishing ages of depositional events, volcanic eruptions, and tephra dispersion. Despite the large literature and the advancements in this research field, the univocal attribution of tephra deposits to specific volcanic sources remains too often elusive. In this contribution, we test the application of a machine learning technique named Support Vector Machine to attempt shedding new light upon tephra deposits related to one of the most complex and debated volcanic regions on Earth: the Pliocene-Pleistocene magmatism in Italy. The machine learning algorithm was trained using one of the most comprehensive global petrological databases (GEOROC); 17 chemical elements including major (SiO2, TiO2, Al2O3, Fe2O3T, CaO, MgO, MnO, Na2O, K2O, P2O5) and selected trace (Sr, Ba, Rb, Zr, Nb, La, Ce) elements were chosen as input parameters. We first show the ability of support vector machines in discriminating among different Pliocene-Pleistocene volcanic provinces in Italy and then apply the same methodology to determine the volcanic source of tephra samples occurring in the Caio outcrop, an Early Pleistocene sedimentary section located in Central Italy. Our results show that: 1) support vector machines can successfully resolve high-dimensional tephrochronological problems overcoming the intrinsic limitation of two- and three-dimensional discrimination diagrams; 2) support vector machines can discriminate among different volcanic provinces in complex magmatic regions; 3) in the specific case study, support vector machines indicate that the most probable source for the investigated tephra samples is the so-called Roman Magmatic Province. These results have strong geochronological and geodynamical implications suggesting new age constraints (1.4 Ma instead of 0.8 Ma) for the starting of the volcanic activity in the Roman Magmatic Province. (C) 2017 Elsevier B.V. All rights reserved.
机译：沉积剖面中火山灰样本的表征、对比和物源测定（火山灰年代学研究）是确定沉积事件、火山喷发和火山灰扩散年龄的有力工具。尽管有大量文献和这一研究领域的进展，但将火山灰矿床单一地归因于特定的火山源仍然常常是难以捉摸的。在这篇文章中，我们测试了一种名为支持向量机的机器学习技术的应用，试图对与地球上最复杂、最有争议的火山区域之一——意大利上新世-更新世岩浆作用——有关的火山岩矿床提供新的认识。机器学习算法使用最全面的全球岩石学数据库（GEOROC）之一进行训练；选择17种化学元素（主要元素（SiO2、TiO2、Al2O3、Fe2O3T、CaO、MgO、MnO、Na2O、K2O、P2O5）和选定的微量元素（Sr、Ba、Rb、Zr、Nb、La、Ce）作为输入参数。我们首先展示了支持向量机在区分意大利不同上新世-更新世火山区方面的能力，然后应用相同的方法确定Caio露头（位于意大利中部的早更新世沉积剖面）中火山灰样本的火山源。我们的研究结果表明：1）支持向量机能够成功地解决高维地温年代学问题，克服了二维和三维判别图的固有局限性；2） 支持向量机可以区分复杂岩浆区的不同火山区；3） 在具体案例研究中，支持向量机表明，所调查的火山灰样本最可能的来源是所谓的罗马岩浆区。这些结果具有强烈的地质年代学和地球动力学意义，表明罗马岩浆区火山活动的开始存在新的年龄限制（1.4 Ma而不是0.8 Ma）。（C） 2017爱思唯尔B.V.版权所有。

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