Planning I/UCRC Virginia Polytechnic Institute and State University: Center for Advanced Subsurface Earth Resource Models

规划 I/UCRC 弗吉尼亚理工学院和州立大学:高级地下地球资源模型中心

基本信息

项目摘要

Mining is intrinsic to modern society's transition to a sustainable existence. Meeting the global demand for earth resources represents a grand challenge for modern society. The Industry-University Cooperative Research Center for Advanced Subsurface Earth Resource Models is a collaborative effort between Colorado School of Mines, Virginia Tech, and industry partners. The planning meeting for this Center, for which this award provides support, focused on developing an integrated approach to locating, characterizing, and visualizing mineral deposits and other earth resources to meet this grand challenge. The intellectual foundation for this Center stems from the unique cross-disciplinary collaborations that have been assembled at the Colorado School of Mines and Virginia Tech including melding expertise in the traditional geoscience disciplines of mineralogy, geochemistry, petrology, economic geology, and geophysics with those in temporal spatial statistics, inverse theory, numerical methods, high performance computing, seismic imaging and inversion, tomographic imaging, and petrophysics. The Center's activities will transform the way geoscience data are used in the exploration and mining industry sector, beginning with the initial mineral exploratoration stage and continuing through mine closure and environmental remediation. Research activities of the Center will fundamentally change the way global exploration and mining of natural resources is currently done, replacing industry experience- and empiricism-based decisions with innovative science and technology-based solutions that inform decision making, increase the chances of exploration success, and reduce financial risk. The goals of the Center will promote socio-economic prosperity and help to reduce the environmental impact of mining. Workforce development is an essential component of the Center activities and will include graduate and undergraduate students, and industry employee participation in research activities and training opportunities. It is anticipated that graduates of the Center will acquire intellectual breadth that will transfer to the mining workforce and this expertise will be essential for the coming transformations that are reshaping the industry. The Center will strengthen and promote cross-disciplinary discoveries in geophysics, geochemistry, mineralogy, computational science and statistics.The Center for Advanced Subsurface Earth Resource Models is focused on advancing the exploration/mining industry sector through the establishment of a cooperative industry-university-National Science Foundation partnership that conducts pre-competitive research and workforce development programs of benefit to industry, academia, and society. The purpose and long-term vision of this Center is directed toward research challenges in the development of 3-D subsurface geologic models for mineral deposits, particularly as these models integrate diverse geoscience data, to inform decision making and minimize geological risk, beginning with locating and mining subsurface earth resources and continuing through mine closure and environmental remediation. Four research thrusts are envisioned: (1) development of novel geophysical and geochemical instrumentation, analysis and interpretation methods for enhanced characterization of rock properties; (2) integration, scaling, and inversion of diverse geological, petrophysical, and geophysical data types of dissimilar spatial resolution and distribution to identify and characterize subsurface earth resources; (3) development of information methodologies for reducing risk associated with decision making; and (4) computational imaging and visualization and development of graphical and exploratory data analysis solutions and visualization tools. Achieving this broad vision requires collaboration of researchers from a broad range of disciplines, including but not limited to economic geologists, geophysicists, statisticians, and computational mathematicians. Complementary expertise in these areas brings Colorado School of Mines and Virginia Tech together with industry partners in an innovative partnership to form a Center focused on the mining sector. With regard to the value that Virginia Tech brings, it has a large and established community of researchers working on the development and applications of computational imaging to applied problems. Research specialties include inverse theory, numerical methods, high performance computing, seismic acquisition, seismic imaging and inversion, tomographic imaging, and petrophysics. In addition, Virginia Tech is home to the largest Mining and Minerals Engineering Department in the country, as well as one of the strongest inverse problems communities in the nation, and strong cross-college expertise in seismology.
采矿业是现代社会向可持续发展过渡的内在要素。满足全球对地球资源的需求是现代社会面临的巨大挑战。高级地下地球资源模型产学合作研究中心是科罗拉多矿业学院、弗吉尼亚理工大学和行业合作伙伴之间的合作成果。该奖项为该中心的规划会议提供了支持,重点是开发一种综合方法来定位、表征和可视化矿藏和其他地球资源,以应对这一重大挑战。该中心的智力基础源于科罗拉多矿业学院和弗吉尼亚理工大学独特的跨学科合作,包括将矿物学、地球化学、岩石学、经济地质学和地球物理学等传统地球科学学科的专业知识与时空统计、反演理论、数值方法、高性能计算、地震成像和反演、层析成像、 和岩石物理学。该中心的活动将改变地球科学数据在勘探和采矿业领域的使用方式,从最初的矿产勘探阶段开始,一直到矿山关闭和环境修复。该中心的研究活动将从根本上改变目前全球自然资源勘探和开采的方式,用基于科学和技术的创新解决方案取代基于行业经验和经验主义的决策,为决策提供信息,增加勘探成功的机会,并降低财务风险。该中心的目标将促进社会经济繁荣,并帮助减少采矿对环境的影响。 劳动力发展是该中心活动的重要组成部分,将包括研究生和本科生以及行业员工参与研究活动和培训机会。 预计该中心的毕业生将获得知识广度,并将其转移到采矿劳动力中,而这些专业知识对于即将重塑行业的变革至关重要。 该中心将加强和促进地球物理学、地球化学、矿物学、计算科学和统计学方面的跨学科发现。高级地下地球资源模型中心致力于通过建立工业-大学-国家科学基金会的合作伙伴关系来推动勘探/采矿业领域的发展,该伙伴关系开展有益于工业界、学术界和社会的竞争前研究和劳动力发展计划。 该中心的目的和长期愿景是针对开发矿床 3D 地下地质模型的研究挑战,特别是当这些模型集成了不同的地球科学数据时,为决策提供信息并最大限度地减少地质风险,从定位和开采地下地球资源开始,一直到矿山关闭和环境修复。 设想了四个研究重点:(1)开发新型地球物理和地球化学仪器、分析和解释方法,以增强岩石特性的表征; (2) 不同空间分辨率和分布的不同地质、岩石物理和地球物理数据类型的整合、缩放和反演,以识别和表征地下地球资源; (3) 开发信息方法以降低决策相关风险; (4)计算成像和可视化以及图形和探索性数据分析解决方案和可视化工具的开发。 实现这一广阔的愿景需要来自广泛学科的研究人员的合作,包括但不限于经济地质学家、地球物理学家、统计学家和计算数学家。这些领域的互补专业知识使科罗拉多矿业学院和弗吉尼亚理工大学与行业合作伙伴建立创新合作伙伴关系,组建一个专注于采矿业的中心。就弗​​吉尼亚理工大学带来的价值而言,它拥有庞大且成熟的研究人员社区,致力于计算成像的开发和应用以解决应用问题。研究专业包括反演理论、数值方法、高性能计算、地震采集、地震成像和反演、层析成像和岩石物理学。此外,弗吉尼亚理工大学拥有全美最大的采矿和矿物工程系,也是全美最强大的反问题社区之一,并且在地震学方面拥有强大的跨学院专业知识。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Matthias Chung其他文献

Kiosk 7R-FB-01 - Optimizing 5D FBee Running Motion Resolved Reconstruction Using Variable Projection Augmented Lagrangian Method
亭 7R-FB-01 - 使用可变投影增广拉格朗日方法优化 5D FBee 运行运动解析重建
  • DOI:
    10.1016/j.jocmr.2024.100804
  • 发表时间:
    2024-03-01
  • 期刊:
  • 影响因子:
    6.100
  • 作者:
    Yitong Yang;Matthias Chung;Jerome Yerly;Davide Piccini;Matthias Stuber;John Oshinski
  • 通讯作者:
    John Oshinski
Image reconstructions using sparse dictionary representations and implicit, non-negative mappings
使用稀疏字典表示和隐式非负映射进行图像重建
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Elizabeth Newman;Jack Michael Solomon;Matthias Chung
  • 通讯作者:
    Matthias Chung
Physics-informed neural networks for predicting liquid dairy manure temperature during storage
用于预测储存期间液态奶牛粪便温度的物理信息神经网络
Population modelling by examples ii
群体建模实例 ii
  • DOI:
    10.22360/summersim.2016.scsc.060
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    5.5
  • 作者:
    Robert J. Smith;Bruce Y. Lee;A. Moustakas;A. Zeigler;M. Prague;Romualdo Santos;Matthias Chung;R. Gras;Valery Forbes;S. Borg;T. Comans;Yifei Ma;N. Punt;W. Jusko;L. Brotz;A. Hyder
  • 通讯作者:
    A. Hyder
Optimal Regularized Inverse Matrices for Inverse Problems
反问题的最优正则逆矩阵

Matthias Chung的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Matthias Chung', 18)}}的其他基金

Collaborative Research: Randomized Numerical Linear Algebra for Large Scale Inversion, Sparse Principal Component Analysis, and Applications
合作研究:大规模反演的随机数值线性代数、稀疏主成分分析及应用
  • 批准号:
    2152661
  • 财政年份:
    2022
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Standard Grant
Collaborative Research: Stochastic Approximations for the Solution and Uncertainty Analysis of Data-Intensive Inverse Problems
合作研究:数据密集型反问题的求解和不确定性分析的随机近似
  • 批准号:
    1723005
  • 财政年份:
    2017
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Standard Grant

相似海外基金

I/UCRC Phase III Iowa State University: Center for Advanced Non-Ferrous Structural Alloys (CANFSA)
I/UCRC 第三期爱荷华州立大学:先进有色金属结构合金中心 (CANFSA)
  • 批准号:
    2137250
  • 财政年份:
    2022
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Standard Grant
I/UCRC Phase II: The Pennsylvania State University: Center for Atomically Thin Multifunctional Coatings (ATOMIC)
I/UCRC 第二阶段:宾夕法尼亚州立大学:原子薄多功能涂层中心 (ATOMIC)
  • 批准号:
    2113864
  • 财政年份:
    2021
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Continuing Grant
Phase II I/UCRC WPI: Center for Robots and Sensors for the Human Well-Being
II 期 I/UCRC WPI:人类福祉机器人和传感器中心
  • 批准号:
    1939061
  • 财政年份:
    2020
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Continuing Grant
Phase I I/UCRC at Florida International University: Center for Wind Hazard and Infrastructure Performance (WHIP)
佛罗里达国际大学 I/UCRC 第一阶段:风灾和基础设施性能中心 (WHIP)
  • 批准号:
    1841503
  • 财政年份:
    2019
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Continuing Grant
I/UCRC Phase II: Collaborative Research: Center for Pharmaceutical Development (CPD)
I/UCRC 第二阶段:合作研究:药物开发中心 (CPD)
  • 批准号:
    1939164
  • 财政年份:
    2019
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Continuing Grant
Phase II I/UCRC [George Mason University]: Center for Spatiotemporal Thinking, Computing and Applications.
II 期 I/UCRC [乔治梅森大学]:时空思维、计算和应用中心。
  • 批准号:
    1841520
  • 财政年份:
    2019
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Continuing Grant
Phase II I/UCRC Harvard: Center for Spatiotemporal Thinking, Computing and Applications (STCA)
II 期 I/UCRC 哈佛大学:时空思维、计算和应用中心 (STCA)
  • 批准号:
    1841403
  • 财政年份:
    2019
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Continuing Grant
Planning I/UCRC at University of Florida Center for Soil Dynamics technologies
佛罗里达大学土壤动力学技术中心规划 I/UCRC
  • 批准号:
    1922477
  • 财政年份:
    2019
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Standard Grant
I/UCRC Phaser III at North Carolina State University: Center for Advanced Forestry Systems
北卡罗来纳州立大学 I/UCRC Phaser III:先进林业系统中心
  • 批准号:
    1916552
  • 财政年份:
    2019
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Continuing Grant
I/UCRC Phase I (Site Addition): Expansion of CICI to Add a New Site at UTA on Sustainable Utilization of Composites in Infrastructure Systems
I/UCRC 第一阶段(站点添加):CICI 扩建,在 UTA 增加一个关于基础设施系统中复合材料可持续利用的新站点
  • 批准号:
    1954073
  • 财政年份:
    2019
  • 资助金额:
    $ 1.5万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了