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，这种记录是稀疏的，往往相当嘈杂和间接。因此，重建过去的环境条件是一项关键和具有挑战性的统计任务。例如，海平面在很大的空间和时间尺度上变化。全球平均海平面变化必须根据稀疏、嘈杂、间接且典型的当地记录来估计，例如盐沼沉积物中保存的化石的生态偏好。在解释其他类型的环境记录（例如温度或降水）时也会出现类似的挑战。现有的统计分析软件包不具备很容易重建过去的环境领域，同时解决一些独特的特点，古环境记录。这些挑战包括在地质材料中可以观察到的东西与它们所反映的环境条件之间的复杂关系，以及地质样品年龄的不确定性。因此，过去的努力通常依赖于定制的、针对特定问题的代码，这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，建立在现代化的、可扩展的机器学习软件基础设施之上，这将使这些方法得到更广泛的使用。 因此，它将有助于将当前全球变化的背景化，并导致对未来全球变化风险的更好预测。该项目还将开发资源，以培训在其研究中寻求采用主题方法的早期职业地球科学家。这些模型提供了一个自然的，概念上简单（但有时计算上具有挑战性）的框架重建过去的环境变量在空间和时间。通常在贝叶斯框架中实现的分层统计模型将导致个体观察结果的多个随机效应划分为多个水平，从而澄清统计分析中的假设。它们将感兴趣的潜在现象及其可变性与观察该潜在过程的噪声机制分开。PaleoSTeHM将开发一个基于Python的框架，用于古数据的时空分层建模。通过在基础层面利用现有的、广泛使用的机器学习框架，PaleoSTeHM将在不修改面向用户的产品的情况下利用当前和未来的计算进步。它将有助于纳入复杂的似然结构，包括嵌入物理模拟模型，该项目由地球科学理事会和高级网络基础设施办公室共同资助，以支持人工智能/该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。