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在沙子和砂岩中自动确定运输和沉积环境
Michael Hasson1, M Colin Marvin1, Mathieu G A Lapôtre1
1Earth and Planetary Sciences Department, Stanford University, Stanford, CA 94305-2115.
概括
一个新的深度神经网络,SandAI,准确地分类沙粒微组织,以揭示运输历史. 这种自动化方法克服了人类的偏见和劳动强度,使得更广泛的地质分析.
科学领域:
- 地质地质地质地质地质地
- 人工智能的人工智能
- 沉积物学的沉积物学
背景情况:
- 沙粒微观结构为它们的运输历史和沉积环境提供了线索.
- 以前的方法依赖于主观的人类分析,非标准分类和大样本大小,限制了它们的广泛使用.
- 对砂粒纹理的自动分析可以克服这些局限性.
研究的目的:
- 开发和验证一个深度神经网络模型,SandAI,用于准确地分类沙粒微纹.
- 为了自动化识别沙粒运输历史的过程.
- 克服主观和劳动密集型手动分析的局限性.
主要方法:
- 一个深度神经网络 (SandAI) 的开发,用于扫描电子显微镜图像的沙粒来自不同的全球环境 (河流,气,冰川,海).
- 在独立的现代石英颗粒和古老的砂岩 (罗纪-普略纪) 上验证了SandAI模型.
- 将SandAI应用于具有争议来源的冷砂岩,以推断古环境条件.
主要成果:
- 根据运输环境,SandAI准确地对砂粒微纹进行了分类.
- 该模型在不同的地质时间段和沉积环境中展示了强度和多功能性.
- 对冷物质样本的成功应用为在雪球地球事件期间的危冰系统提供了洞察力.
结论:
- SandAI提供了一种快速,自动化和准确的方法来分析沙粒运输历史.
- 该模型克服了以前的局限性,使其在沉积物学和古生物学中具有更广泛的应用.
- 这项技术增强了我们对地球过去环境和地质过程的理解.
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