捕捉专家的不确定性:根据ICC的信息,对火山地震性进行软标签
Sam Mitchinson1, Jessica H Johnson1, Ben Milner2
1School of Environmental Sciences, University of East Anglia, Norwich, UK.
概括
这项研究引入了火山地震信号的ICC信息软标签,通过量化专家分歧来提高机器学习的准确性. 这种方法通过捕捉分类不确定性来增强火山监测和喷发预测.
科学领域:
- 地质物理学 地质物理学
- 地震学 地震学
- 机器学习 机器学习
背景情况:
- 火山地震信号的分类对于监测和爆发预测至关重要.
- 传统方法可能无法考虑专家判断的变化和不确定性.
- 在地震数据分析中,专家间的协议往往被忽视.
研究的目的:
- 开发一种用于量化火山地震信号分类的专家间协议的新方法.
- 将该协议措施纳入机器学习的概率,ICC-informed软标签中.
- 提高火山学中的机器学习模型的准确性,稳定性和可转移性.
主要方法:
- 一项由89位专家进行的全球调查,对来自新西兰鲁阿佩胡的80个火山地震事件进行了分类.
- 利用类内相关系数 (ICC) 来量化专家间的一致性.
- 开发了一种软标签方法,根据ICC分数加权类概率.
主要成果:
- 单级评级得分显示,即使对于已建立的火山构造 (VT) 和长期 (LP) 分类,一致性也很差.
- 结合多个专家评级,显著提高了VT和LP信号的可靠性.
- 对于混合 (HYB) 和其他 (OT) 类别,仍然存在大量的专家分歧.
- 由ICC告知的软标签有效地捕捉并反映了专家的不确定性.
结论:
- 根据ICC的信息,软标签通过明确捕捉分类不确定性,为硬标签提供了强大的替代方案.
- 这种概率方法可以显著提高火山监测中的机器学习模型性能.
- 该方法代表了自动化框架的火山地震数据的标签和解释的根本性转变.
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