IN013
Big Data and Data Mining in Deep Time
Session ID#: 83144
Session Description:
Big data is becoming increasingly important in almost every science domain. Earth science is an interdisciplinary field that utilizes tons of chemical, physical and descriptive data across the ~4.5 billion years on Earth. Big-data-driven methods, such as machine learning, have been successfully applied in many aspects of science, such as biology and medicine. Those methods have started to be applied in geoscience to help explore information hidden in Earth’s history and test various hypotheses. To summarize and present the state-of-the art progress in big data and machine learning within the fields of deep time Earth science, we propose this session. We welcome studies related to solving geological questions using data-driven methods, techniques and the cyberinfrastructure, with a particular focus on the deep time. Our session aims at accelerating the pace of coping with data-related geological questions, perspectives and introduction of on-going projects of building synthetic databases for studying Earth’s evolution.
Index Terms:
1908 Cyberinfrastructure [INFORMATICS]
1914 Data mining [INFORMATICS]
1916 Data and information discovery [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Chao Ma, University of Idaho, Computer Science, Moscow, ID, United States
Conveners: Xiaogang Ma, University of Idaho, Moscow, ID, United States, Shuang Zhang, Department of Geology and Geophysics, Yale University, New Haven, CT, United States and Jihua Hao, Laboratoire de Géologie de Lyon, Observatoire de Lyon, Université Claude Bernard Lyon1, Villeurbanne Cedex, France
Primary Liaison: Chao Ma, University of Idaho, Computer Science, Moscow, ID, United States
Quantitative evidence for a Neoproterozoic glacial origin of the Great Unconformity (Invited) (492713)
C. Brenhin Keller, Dartmouth College, Hanover, CA, United States, Jon M Husson, University of Victoria, Victoria, Canada, Ross Mitchell, Earth Dynamics Research Group, ARC Centre of Excellence for Core to Crust Fluid Systems (CCFS) and The Institute for Geoscience Research (TIGeR), School of Earth and Planetary Sciences, Curtin University, Perth, Australia, William F Bottke Jr, Southwest Research Institute Boulder, Solar System Exploration Research Virtual Institute, Institute for the Science of Exploration Targets, Boulder, CO, United States, Thomas Gernon, National Oceanography Centre, Glasgow, United Kingdom, Patrick Boehnke, Eta Vision, Chicago, IL, United States, Elizabeth A Bell, UCLA, Los Angeles, CA, United States, Nicholas Swanson-Hysell, University of California Berkeley, Earth and Planetary Science, Berkeley, CA, United States and Shanan E Peters, University of Wisconsin Madison, Geoscience, Madison, WI, United States
Text Mining to Facilitate Geoscience Knowledge Discovery (509584)
Chengbin Wang, Wuhan, Hubei, China, Xiaogang Ma, University of Idaho, Moscow, ID, United States and Jianguo Chen, China University of Geosciences, Wuhan, China
Predicting unknown mineral localities based on mineral associations (567149)
Anirudh Prabhu1, Shaunna M Morrison2, Ahmed Eleish1, Shweta Narkar1, Peter Arthur Fox1, Joshua J Golden3, Robert T Downs4, Samuel Perry5, Peter C Burns6, Jolyon Ralph7, Simone E. Runyon8 and Robert Hazen2, (1)Rensselaer Polytechnic Institute, Tetherless World Constellation, Troy, NY, United States, (2)Carnegie Institution for Science, Geophysical Laboratory, Washington, DC, United States, (3)University of Arizona, Tucson, AZ, United States, (4)University of Arizona, Department of Geosciences, Tucson, AZ, United States, (5)University of Notre Dame, Notre Dame, United States, (6)University of Notre Dame, Civil & Environmental Engineering & Earth Sciences, South Bend, IN, United States, (7)mindat.org, Surray, United Kingdom, (8)University of Wyoming, Department of Geology and Geophysics, Laramie, WY, United States
Spatiotemporal Distribution of Global Porphyry Copper Deposits Revealed by Network Analysis (583326)
Molei Zhao1, Qiuming Cheng2, Zhenjie Zhang1 and Jie Yang3, (1)China University of Geosciences (Beijing), School of Earth Sciences and Resources, Beijing, China, (2)China University of Geosciences (Beijing), The State Key Lab of Geological Processes and Mineral Resources, Beijing, China, (3)China University of Geosciences (Beijing), Institute of Earth Sciences, Beijing, China
