EarthCube Data Infrastructure: Collaborative Proposal: A unified experimental-natural digital data system for analysis of rock microstructures

从微观上看，岩石揭示的结构有助于解释形成它们的过程和作用力。这些微观结构有助于解释在山脉和构造板块规模上发生的现象。在已知的压力、温度、应力、应变和应变率条件下，通过与实验中形成的微结构进行比较，有助于解释自然变形过程中形成的微观结构，而实验岩石变形则得益于通过与自然变形岩石进行比较而提供的地面事实。然而，由于缺乏任何包含这些数据的数据库，搜索相关的自然或实验变形的微结构的能力受到阻碍。参与这一项目的研究人员将为岩石微观结构开发一个单一的数字数据系统，以促进研究自然和实验变形岩石的社区之间的关键互动。为了帮助比较自然界和实验中形成的微观结构，我们将连接常用的分析工具，并开发一个使用机器学习进行微观结构自动比较的试点项目。岩石微结构将微观尺度上的过程与露头、造山带和板块尺度上的现象联系起来，揭示了应力、应变和应变率之间的关系。定量的流变信息是通过将自然形成的微结构与已知条件下的岩石变形实验产生的微结构联系起来研究而获得的。该项目将为自然和实验变形的岩石微结构数据开发一个单一的数字数据系统，以便于比较来自不同环境的微结构。一个相互关联的数据系统将促进实验变形从业人员、研究自然变形的从业人员和计算机科学界之间的互动。该数据系统将利用目前正在开发的构造地质和构造领域的StraboSpot数据系统。开发这一系统需要：1)修改StraboSpot数据系统，以接受来自自然和实验变形岩石的微结构数据；以及2)将微结构数据与其地质背景联系起来？无论是在自然界，还是它的实验数据/参数。研究人员将与岩石变形界接触，目标是建立数据收集的数据标准和协议，并将我们的工作与正在进行的建立自动元数据收集和数字数据存储的协议和技术的工作相结合。为了分析这些社区研究和/或生成的微结构，我们将确保StraboSpot数据输出与常用的微结构工具兼容。他们将开发一个试点项目，利用机器学习来比较和分析来自不同环境的微结构。

When viewed at the micro-scale, rocks reveal structures that help to interpret the processes and forces responsible for their formation. These microstructures help to explain phenomena that occur at the scale of mountains and tectonic plates. Interpretation of microstructures formed in nature during deformation is aided by comparison with those formed during experiments, under known conditions of pressure, temperature, stress, strain and strain rate, and experimental rock deformation benefits from the ground truth offered through comparison with rocks deformed in nature. However, the ability to search for relevant naturally or experimentally deformed microstructures is hindered by the lack of any database that contains these data. The researchers collaborating on this project will develop a single digital data system for rock microstructures to facilitate the critical interaction between and among the communities that study naturally and experimentally deformed rocks. To aid in the comparison of microstructures formed in nature and experiment, we will link to commonly used analytical tools and develop a pilot project for automatic comparison of microstructures using machine learning. Rock microstructures relate processes at the microscopic scale to phenomena at the outcrop, orogen, and plate scales and reveal the relationships among stress, strain, and strain rate. Quantitative rheological information is obtained through linked studies of naturally formed microstructures with those created during rock deformation experiments under known conditions. The project will develop a single digital data system for both naturally and experimentally deformed rock microstructure data to facilitate comparison of microstructures from different environments. A linked data system will facilitate interaction between practitioners of experimental deformation, those studying natural deformation and the cyberscience community. The data system will leverage the StraboSpot data system currently under development in Structural Geology and Tectonics. To develop this system requires: 1) Modification of the StraboSpot data system to accept microstructural data from both naturally and experimentally deformed rocks; and 2) Linking the microstructural data to its geologic context ? either in nature, or its experimental data/parameters. The researchers will engage the rock deformation community with the goal of establishing data standards and protocols for data collection, and integrate our work with ongoing efforts to establish protocols and techniques for automated metadata collection and digital data storage. To analyze the microstructures studied and/or generated by these communities, we will ensure StraboSpot data output is compatible with commonly used microstructural tools. They will develop a pilot project for comparing and analyzing microstructures from different environments using machine-learning.