Jutzeler Martin, Carey Rebecca J, Dagasan Yasin, McNeill Andrew, Cas Ray A F
Centre for Ore Deposit and Earth Sciences, School of Natural Sciences, University of Tasmania, Hobart, Australia.
Datarock Pty Ltd, Melbourne, Australia.
Sci Rep. 2024 Dec 30;14(1):31793. doi: 10.1038/s41598-024-82847-0.
Volcanic stratigraphy reconstruction is traditionally based on qualitative facies analysis complemented by geochemical analyses. Here we present a novel technique based on machine learning identification of crystal size distribution to quantitatively fingerprint lavas, shallow intrusions and coarse lava breccias. This technique, based on a simple photograph of a rock (or core) sample, is complementary to existing methods and allows another strategy to identify and compare volcanic rocks for stratigraphic correlation. Phenocryst size distributions display overall homogeneity within one volcanic body but may vary considerably between igneous bodies. Restricted to shallow intrusions and volcanic lavas, this concept allows for stratigraphic fingerprinting of volcanic rocks in poorly exposed, up to moderately altered, and/or complexly tectonized formations. We built an automated image analysis workflow using machine-learning for crystal segmentation, followed by statistical analysis of physical descriptors to compare and match the size distribution of feldspar phenocrysts. The workflow comprises three instance segmentation models for pre-processing the images, automated scale measurement and grain segmentation using Mask R-CNN. This avoids the laborious and time-consuming task of manual picking by image analysis, and allows for a rapid, unbiased and quantitative approach to determine crystal size distribution (CSD). Our volcanic architecture reconstruction of multiple dacite bodies in the mineralized Cambrian Mt Read Volcanics in Tasmania, Australia, is independently validated by bulk-rock chemical analyses of key samples. This volcanic stratigraphy method can be applied to a large variety of igneous rocks and is complementary to geochemical analyses and qualitative crystal content assessment.
传统上，火山地层重建基于定性相分析，并辅以地球化学分析。在此，我们提出一种基于机器学习识别晶体尺寸分布的新技术，用于定量识别熔岩、浅成侵入体和粗粒熔岩角砾岩。该技术基于岩石（或岩芯）样本的简单照片，是对现有方法的补充，并为地层对比识别和比较火山岩提供了另一种策略。斑晶尺寸分布在一个火山体内总体上是均匀的，但在不同火成岩体之间可能有很大差异。限于浅成侵入体和火山熔岩，这一概念使得在露头不佳、蚀变程度达中等及以上和/或构造复杂的地层中对火山岩进行地层指纹识别成为可能。我们构建了一个使用机器学习进行晶体分割的自动化图像分析工作流程，随后对物理描述符进行统计分析，以比较和匹配长石斑晶的尺寸分布。该工作流程包括三个实例分割模型，用于图像预处理、自动尺度测量和使用Mask R-CNN进行颗粒分割。这避免了图像分析中人工挑选这一费力且耗时的任务，并允许采用快速、无偏差的定量方法来确定晶体尺寸分布（CSD）。我们对澳大利亚塔斯马尼亚矿化寒武纪里德山火山岩中多个英安岩体的火山结构重建，通过对关键样本的全岩化学分析得到了独立验证。这种火山地层学方法可应用于多种火成岩，是对地球化学分析和定性晶体含量评估的补充。
