Doctoral Dissertation Research: Predicting the location of hominin cave fossil sites with a machine learning approach

博士论文研究：利用机器学习方法预测古人类洞穴化石遗址的位置

• 批准号: 2341328
• 负责人: Juliet Brophy
• 金额: $ 2.99万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341328&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Predicting; location

Surveying an area for new fossil sites is a time- and labor-intensive activity. However, machine learning models trained in remotely sensed imagery can be used to predict the location of these sites. The purpose of this research is to build a predictive machine learning model for cave sites suited to the unique environmental and preservation context hominin fossil cave sites. This doctoral dissertation research seeks to (1) determine which environmental and geomorphological variables are most influential in the distribution of caves in this context, and (2) transform the process by which cave fossil sites are discovered by integrating remote sensing and machine learning techniques. This is an interdisciplinary project that supports graduate training and research in STEM and seeks to promote future collaborative projects via online data sharing, conference talks, publications, and public engagement.In this project, remotely sensed high-resolution images of the study area that document surface reflection and elevation, in addition to geologic data, serve as the sources for input predictor variables in machine learning prediction models. The models use these inputs to identify complex patterns between characteristics of Plio-Pleistocene hominin cave sites and locate areas with a similar suite of features for fossil prospecting. This research compares different machine learning algorithms to determine which one is best suited to predict cave sites within this context, conducts in-field ground-truthing assessments of the best performing model, and analyzes model utility outside the study area. This project examines the geomorphological characteristics that indicate subterranean fossil sites at the surface level and seeks out new potential localities for fossil prospecting. This quantitative approach helps reduce the barriers impeding site discovery and aims to increase fossil recovery in paleoanthropology. This project is jointly funded by the Biological Anthropology Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

勘察一个地区是否有新的化石地点是一项耗时和劳动密集型的活动。然而，在遥感图像中训练的机器学习模型可用于预测这些地点的位置。这项研究的目的是为洞穴遗址建立一个预测机器学习模型，该模型适合独特的环境和保护背景的古人类化石洞穴遗址。本博士论文研究旨在（1）确定哪些环境和地貌变量对这种情况下的洞穴分布影响最大，（2）通过整合遥感和机器学习技术来改变洞穴化石遗址的发现过程。这是一个跨学科项目，支持 STEM 的研究生培训和研究，并寻求通过在线数据共享、会议演讲、出版物和公众参与来促进未来的合作项目。在该项目中，除了地质数据之外，记录表面反射和高程的研究区域的遥感高分辨率图像也作为机器学习预测模型中输入预测变量的来源。这些模型使用这些输入来识别上里奥-更新世人类洞穴遗址特征之间的复杂模式，并找到具有相似特征的区域进行化石勘探。这项研究比较了不同的机器学习算法，以确定哪种算法最适合在这种情况下预测洞穴遗址，对性能最佳的模型进行现场地面实况评估，并分析研究区域外的模型效用。该项目研究了表明地表地下化石遗址的地貌特征，并寻找新的潜在化石勘探地点。这种定量方法有助于减少阻碍遗址发现的障碍，旨在提高古人类学中的化石回收率。该项目由生物人类学计划和刺激竞争性研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。