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

当在微观尺度上观察时，岩石揭示的结构有助于解释其形成的过程和力量。这些微观结构有助于解释发生在山脉和构造板块尺度上的现象。通过与已知压力、温度、应力、应变和应变速率条件下的实验结果进行比较，可以帮助解释变形过程中自然形成的微观结构，而通过与自然变形的岩石进行比较，可以获得真实的岩石变形实验结果。然而，由于缺乏包含这些数据的数据库，搜索相关的自然或实验变形微观结构的能力受到阻碍。在这个项目上合作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，我们将链接到常用的分析工具，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。岩石微观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。定量流变学信息是通过将自然形成的微观结构与已知条件下岩石变形实验中产生的微观结构相联系的研究获得的。该项目将为自然和实验变形的岩石微观结构数据开发一个单一的数字数据系统，以便于比较不同环境下的微观结构。关联数据系统将促进实验变形实践者、研究自然变形者和网络科学界之间的互动。该数据系统将利用目前正在开发中的构造地质学和构造学StraboSpot数据系统。开发该系统需要：1)对StraboSpot数据系统进行改进，使其能够接受自然和实验变形岩石的微观结构数据；2)将微观构造数据与地质背景联系起来？无论是在自然界，还是其实验数据/参数。研究人员将与岩石变形界合作，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相结合。为了分析这些群落研究和/或产生的微观结构，我们将确保StraboSpot数据输出与常用的微观结构工具兼容。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观结构。

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.