机器学习的晶体大小分布用于火山地层学相关联的相关联
Martin Jutzeler1, Rebecca J Carey2, Yasin Dagasan3
1Centre for Ore Deposit and Earth Sciences, School of Natural Sciences, University of Tasmania, Hobart, Australia. jutzeler@gmail.com.
Scientific reports
|December 31, 2024
概括
机器学习量化了从岩石照片到指纹火山岩石的晶体大小分布. 这种新的方法有助于复杂的地质构造中的分层学相关性.
科学领域:
- 火山学 火山学是一门学科.
- 地质地质地质地质地质地
- 机器学习应用 机器学习应用
背景情况:
- 传统的火山层表学依赖于定性地表和地化学分析.
- 需要使用定量方法来关联火山单位,特别是在具有挑战性的地形上.
研究的目的:
- 引入一种基于机器学习的新技术,用于对火山岩的定量指纹.
- 从岩石图像中使用晶体大小分布 (CSD) 分析来实现平流图相关性.
主要方法:
- 开发了一种使用机器学习 (Mask R-CNN) 进行晶体细分的自动化图像分析工作流.
- 运用物理描述符的统计分析来比较田晶体尺寸分布.
- 在塔斯马尼亚州雷德山火山山脉的石体上验证了该技术.
主要成果:
- 晶尺寸分布在火山体内是均的,但在火山体之间有所不同,因此可以进行指纹识别.
- 自动化工作流提供了一个快速,公正和定量的方法来确定CSD.
- 该方法成功重建了火山结构,并通过地球化学分析验证了这一点.
结论:
- 中央证券交易所的机器学习识别为火山岩的识别和分层学相关性提供了一个互补和定量策略.
- 这种技术适用于暴露不良,改变或构造形成的各种岩石.
- 该方法通过提供客观的,基于图像的岩石表征来增强传统方法.
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