EAR-Climate: Catalytic: A Modern Spatio-Temporal Hierarchical Modeling Framework for Paleo-Environmental Data (PaleoSTeHM)

EAR-Climate：催化：古环境数据的现代时空分层建模框架 (PaleoSTeHM)

• 批准号: 2148265
• 负责人: Robert Kopp
• 金额: $ 80.08万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148265&HistoricalAwards=false
• 关键词: EAR; Climate; Catalytic; Modern; Spatio

The geological record provides the only long-term archive of environmental change and variability. Yet that record is sparse and often quite noisy and indirect. Reconstructing past environmental conditions is thus a critical and challenging statistical task. For example, sea level varies over a broad range of spatial and temporal scales. Global average sea-level change must be estimated from sparse, noisy, indirect and typically local records, such as the ecological preferences of fossils preserved in salt marsh sediments. Similar challenges arise in the interpretation of other types of environmental records (for example, of temperature or precipitation). Existing statistical analysis packages are not equipped to easily reconstruct past environmental fields while addressing some of the distinctive characteristics of paleo-environmental records. These challenges include the complex relationships between what can be observed in geological materials and the environmental conditions that they reflect, and the uncertainties that characterize the age of geological samples. Past efforts have thus generally relied on custom-built, problem-specific code, which has limited the adoption of state-of-the-art analysis approaches. The PaleoSTeHM project will therefore develop a new software framework, built on top of modern, scalable software infrastructure for machine learning, that will enable broader use of these approaches. It will thus facilitate the contextualization of current global change and lead to improved projections of future global change risk. The project will also develop resources to train early-career Earth scientists seeking to employ theme methods in their research.The central concept in PaleoSTeHM is that of the spatio-temporal hierarchical statistical model. These models provide a natural, conceptually straightforward (but sometimes computationally challenging) framework for reconstructing past environmental variables over space and time. Hierarchical statistical models, which are frequently implemented in a Bayesian framework, partition the multiple random effects that lead to individual observations into levels, thus clarifying the assumptions in a statistical analysis. They separate the underlying phenomenon of interest and its variability from the noisy mechanisms by which this underlying process is observed. PaleoSTeHM will develop a Python-based framework for spatio-temporal hierarchical modeling of paleodata. By leveraging an existing, widely used machine-learning framework at the base level, PaleoSTeHM will be built to take advantage of current and future computational advances without modifications to the user-facing product. It will facilitate the incorporation of complex likelihood structures, including the embedding of physical simulation models, and thus pave the way for further advances in paleo-modeling.This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.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.

地质记录提供了环境变化和可变性的唯一长期档案。然而，该记录稀疏，通常很嘈杂和间接。因此，重建过去的环境条件是一项至关重要且具有挑战性的统计任务。例如，海平面在广泛的空间和时间尺度上有所不同。必须根据稀疏，嘈杂，间接和通常的局部记录估算全球平均海平面变化，例如保存在盐沼泽沉积物中的化石的生态偏好。解释其他类型的环境记录（例如，温度或降水）也会出现类似的挑战。现有的统计分析软件包不具备轻松地重建过去的环境领域，同时解决了古环境记录的某些独特特征。这些挑战包括在地质材料中可以观察到的内容与它们所反映的环境条件之间的复杂关系，以及表征地质样本时代的不确定性。因此，过去的努力通常依赖于定制的，特定于问题的代码，该代码限制了最先进的分析方法的采用。因此，Paleostehm项目将开发一个新的软件框架，该框架建立在用于机器学习的现代，可扩展的软件基础架构之上，这将使这些方法更广泛地使用。 因此，它将促进当前全球变化的情境化，并改善未来全球变化风险的预测。该项目还将开发资源来培训寻求在其研究中采用主题方法的早期地球科学家。Paleostehm中的中心概念是时空层次统计模型的中心概念。这些模型在概念上（但有时甚至在计算上具有挑战性）提供了一个自然的，可用于重建过去和时间上过去的环境变量。经常在贝叶斯框架中实现的层次统计模型，将多个随机效应划分为导致单个观察到水平的多个随机效应，从而阐明了统计分析中的假设。他们将感兴趣的基本现象及其可变性分开，并观察到这种基本过程的嘈杂机制。 Paleostehm将开发一个基于Python的框架，用于古代植物的时空层次建模。通过利用基本级别的现有，广泛使用的机器学习框架，将建立Paleostehm，以利用当前和将来的计算进步，而不会对面向用户的产品进行修改。它将促进复杂的可能性结构的结合，包括物理模拟模型的嵌入，因此为古模型的进一步进步铺平了道路。该项目是由地球科学局与高级Cyber​​infrasture办公室合作的共同资助的，以支持AI/ML的科学活动，并在宣传中宣布了这一点。通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。