Barcoding Magmas: Applying Machine Learning In Zircon Geochemistry To Increase Provenance Accuracy

岩浆条形码：在锆石地球化学中应用机器学习以提高来源准确性

• 批准号: NE/X011259/1
• 负责人: Eddie Dempsey
• 金额: $ 10.29万
• 依托单位: University of Hull
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX011259%2F1
• 关键词: Barcoding; Magmas; Applying; Machine; Learning

Sedimentary provenance allows researchers to reconstruct and interpret the history of a sediment from its parent rock (source) to its deposition (sink). The "sources" are eroding mountains and the "sinks" are sedimentary basins. Sedimentary provenance studies can allow us to reconstruct past environments, climate and tectonics. It allows us to assess the suitability of sedimentary rocks for resource exploration (hydrocarbons, geothermal, aquifers) or carbon capture/storage (CCS). A key feature of mountain building events (orogenesis) is the emplacement of large bodies of molten rock (magma) which cools and crystallises as igneous rocks, in particular granite. Granites are primarily composed of quartz, feldspar, amphibole and/or mica. In addition to these, a common mineral, which grows within the magma during emplacement, is zircon.Zircon is a remarkable material. It's easily datable using Uranium-Lead (U-Pb) geochronology, yielding the age of the igneous body in which it formed. It is physically and chemically robust, withstanding alteration during erosion, transport and later diagenesis (physical and chemical changes in sediments after their deposition). Traditionally, provenance studies are conducted by collecting large suites of zircons from sediments, dating them (U-Pb geochronology) and matching the acquired ages to previously dated geological regions. This gives a very broad picture of the source of the sediment but lacks accuracy. For example, zircons from the Chinese Loess Plateau are sourced from granites in the ~2.5 million km2 Tibetan Plateau. Closer to home, the Carboniferous age sedimentary basin of North West Ireland sources material from Caledonian Granites in Co. Donegal. However, these granites are part of a suite of coeval granites found throughout the ~92,000 km2 Caledonian mountain belt in Britain and Ireland. As a result, links between these sediments and the Donegal granites remains speculative, and existing models of sediment transport may be highly inaccurate.During crystallisation in the magma chamber, zircons incorporate trace elements which geochemically record the changing conditions in the magma (much like tree rings record the passing of the seasons). Each individual zircon only records part of the geochemical history of that magma chamber from when it was crystalizing. Therefore, to reconstruct a more complete geochemical history of the magma many zircons must be analysed. We propose to use machine learning and pattern matching algorithms to analyse the growth patterns and trace element distribution within 10,000 individual zircons from the five granite bodies (2000 per body) making up the Donegal Batholith. This analysis will create a geochemical barcode, representing a continuous timeline of conditions within their corresponding magma chambers. Because magma bodies are natural and dynamic systems, no two bodies will have an identical geochemical signature, so the generated barcode will be unique to each. Zircons from sediment in the Carboniferous sandstones of NW Ireland believed to derive from the orogenic belt containing our magmatic systems will be geochemically analysed using the same techniques. Each of these zircons will contain a partial barcode of its parent magmatic body. Using bespoke pattern matching algorithms, these partial barcodes will be compared to new barcodes of the nearby granite bodies to ascertain their exact source & reconstruct the regional sediment transport pathways during the Carboniferous. In essence, this will be akin to matching a partial DNA sequence from the zircons in sediment to the complete DNA genomes of the nearby igneous units.This research brings together a UK-based team of international scientists, Dr Dempsey & Dr Bird (University of Hull) and, Dr Einsle & Dr Neill (University of Glasgow). Our team combines a wealth of experience of Caledonian tectonics & magmatism, zircon geochemistry & provenance, exceptional observational & analytical skills.

沉积物源使研究人员能够重建和解释沉积物从母岩（源）到沉积（汇）的历史。“源”是侵蚀山脉，“汇”是沉积盆地。沉积物源研究可以让我们重建过去的环境、气候和构造。它使我们能够评估沉积岩对资源勘探（碳氢化合物，地热，含水层）或碳捕获/储存（CCS）的适用性。造山运动的一个关键特征是大量熔融岩石（岩浆）的就位，这些岩浆冷却并结晶为火成岩，特别是花岗岩。花岗岩主要由石英、长石、角闪石和/或云母组成。除此之外，还有一种常见的矿物是锆石，它在侵位过程中生长在岩浆中。使用铀铅（U-Pb）地质年代学很容易确定它的年龄，从而得出它形成的火成岩体的年龄。它在物理和化学上是坚固的，能够承受侵蚀、搬运和后来的成岩作用（沉积物沉积后的物理和化学变化）期间的变化。传统上，物源研究是通过从沉积物中收集大量的锆石，对其进行测年（U-Pb地质年代学），并将获得的年龄与以前测年的地质区域相匹配。这提供了沉积物来源的非常广泛的情况，但缺乏准确性。例如，中国黄土高原的锆石来源于约250万平方公里的青藏高原的花岗岩。在离家更近的地方，爱尔兰西北部的石炭纪沉积盆地的物质来源于Co.多尼加尔的加里东花岗岩。然而，这些花岗岩是在英国和爱尔兰约92，000平方公里的喀里多尼亚山脉中发现的一套同时代花岗岩的一部分。因此，这些沉积物和多尼加尔花岗岩之间的联系仍然是推测性的，现有的沉积物迁移模型可能非常不准确。在岩浆房结晶过程中，锆石含有微量元素，这些元素从地球化学上记录了岩浆中变化的条件（就像树木的年轮记录了季节的流逝）。每一个单独的锆石只记录了岩浆房结晶时的部分地球化学历史。因此，为了重建更完整的岩浆地球化学史，必须对许多锆石进行分析。我们建议使用机器学习和模式匹配算法来分析构成多尼加尔岩的五个花岗岩体（每个岩体2000个）中10,000个锆石的生长模式和微量元素分布。这种分析将创建一个地球化学条形码，代表其相应岩浆房内条件的连续时间轴。由于岩浆体是自然的动态系统，没有两个岩浆体会有相同的地球化学特征，因此生成的条形码对每个岩浆体都是唯一的。爱尔兰西北部石炭纪砂岩中的沉积物中的锆石被认为来自包含我们的岩浆系统的造山带，将使用相同的技术进行地球化学分析。这些锆石中的每一个都将包含其母岩浆体的部分条形码。使用定制的模式匹配算法，这些部分条形码将与附近花岗岩体的新条形码进行比较，以确定它们的确切来源并重建石炭纪期间的区域沉积物运输路径。从本质上讲，这将类似于将沉积物中锆石的部分DNA序列与附近火成岩单元的完整DNA基因组进行匹配。这项研究汇集了一个由国际科学家组成的英国团队，Dempsey博士和Bird博士（船体大学）以及Einsle博士和Neill博士（格拉斯哥大学）。我们的团队结合了加里东构造和岩浆活动、锆石地球化学和来源的丰富经验，以及卓越的观察和分析技能。