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

地质记录提供了环境变化和变率的唯一长期档案。然而，这种记录是稀疏的，而且往往相当嘈杂和间接。因此，重建过去的环境状况是一项关键而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变化。全球平均海平面变化必须从稀疏的、嘈杂的、间接的和典型的局部记录来估计，比如保存在盐沼沉积物中的化石的生态偏好。在解释其他类型的环境记录（例如温度或降水）时也出现了类似的挑战。现有的统计分析软件包在处理古环境记录的一些独特特征时，不能很容易地重建过去的环境场。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，以及地质样品年龄特征的不确定性。因此，过去的工作通常依赖于定制的、特定于问题的代码，这限制了采用最先进的分析方法。因此，palestehm项目将开发一个新的软件框架，建立在现代的、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，它将有助于对当前全球变化进行背景分析，并有助于改进对未来全球变化风险的预测。该项目还将开发资源，培训寻求在其研究中采用主题方法的早期职业地球科学家。古stemm的核心概念是时空分层统计模型。这些模型提供了一个自然的、概念上直截了当的（但有时在计算上具有挑战性）框架，用于在空间和时间上重建过去的环境变量。通常在贝叶斯框架中实现的分层统计模型，将导致单个观察的多个随机效应划分为多个级别，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这一潜在过程的嘈杂机制分开。palaestehm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，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来构建palestehm。它将促进复杂似然结构的整合，包括物理模拟模型的嵌入，从而为古建模的进一步发展铺平道路。该项目由地球科学理事会和先进网络基础设施办公室共同资助，以支持地球科学领域的人工智能/机器学习和开放科学活动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.