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

EarthCube数据基础设施：协作提案：用于分析岩石微观结构的统一实验自然数字数据系统

• 批准号: 1639710
• 负责人: Chris Marone
• 金额: $ 6.37万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1639710&HistoricalAwards=false
• 关键词: EarthCube; Data; Infrastructure; Collaborative; Proposal

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.

当在微观尺度上观察时，岩石揭示了有助于解释其形成过程和力量的结构。 这些微结构有助于解释发生在山脉和构造板块尺度上的现象。 在已知的压力、温度、应力、应变和应变率条件下，通过与实验期间形成的微观结构进行比较，有助于解释变形期间在自然界中形成的微观结构，并且实验岩石变形受益于通过与自然界中变形的岩石进行比较而提供的基础事实。 然而，由于缺乏包含这些数据的任何数据库，搜索相关的自然或实验变形的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开发一个岩石微观结构的单一数字数据系统，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自动比较微观结构的试点项目。 岩石微结构将微观尺度的过程与露头、造山带和板块尺度的现象联系起来，并揭示应力、应变和应变率之间的关系。 定量流变学信息是通过对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进行关联研究而获得的。 该项目将为自然和实验变形岩石微结构数据开发一个单一的数字数据系统，以便于比较不同环境下的微结构。 一个链接的数据系统将促进实验变形的从业者之间的互动，那些研究自然变形和网络科学界。 该数据系统将利用目前正在结构地质学和构造学领域开发的StraboSpot数据系统。 为了开发这个系统需要：1）修改的StraboSpot数据系统，接受微观结构数据从自然和实验变形的岩石;和2）连接的微观结构数据，其地质背景？无论是在自然界中，还是在其实验数据/参数中。 研究人员将与岩石变形社区合作，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努力相结合，以建立自动元数据收集和数字数据存储的协议和技术。 为了分析这些社区研究和/或生成的微观结构，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，使用机器学习比较和分析来自不同环境的微观结构。

项目成果

Similarity of fast and slow earthquakes illuminated by machine learning

• DOI: 10.1038/s41561-018-0272-8
• 发表时间: 2019-01-01
• 期刊: NATURE GEOSCIENCE
• 影响因子: 18.3
• 作者: Hulbert, Claudia;Rouet-Leduc, Bertrand;Marone, Chris
• 通讯作者: Marone, Chris

Preseismic Fault Creep and Elastic Wave Amplitude Precursors Scale With Lab Earthquake Magnitude for the Continuum of Tectonic Failure Modes

构造破坏模式连续体的地震断层蠕变和弹性波振幅前兆尺度与实验室地震震级

• DOI: 10.1029/2020gl086986
• 发表时间: 2020
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Shreedharan, Srisharan;Bolton, David Chas;Rivière, Jacques;Marone, Chris
• 通讯作者: Marone, Chris

Evolution of Elastic and Mechanical Properties During Fault Shear: The Roles of Clay Content, Fabric Development, and Porosity

断层剪切过程中弹性和力学性能的演变：粘土含量、织物发育和孔隙度的作用

• DOI: 10.1029/2019jb018612
• 发表时间: 2020
• 期刊: Journal of Geophysical Research: Solid Earth
• 作者: Kenigsberg, Abby R.;Rivière, Jacques;Marone, Chris;Saffer, Demian M.
• 通讯作者: Saffer, Demian M.

Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning

• DOI: 10.1785/0220180367
• 发表时间: 2019-05-01
• 期刊: SEISMOLOGICAL RESEARCH LETTERS
• 影响因子: 3.3
• 作者: Bolton, David C.;Shokouhi, Parisa;Johnson, Paul A.
• 通讯作者: Johnson, Paul A.