IN031
Geospatial Intelligence: Data-Driven Research in the Earth Sciences
Session ID#: 80929
Session Description:
Amount and variety of data created in the process of measuring and modelling Earth’s subsystems is drastically increasing with ever-improving computational and data acquisition capabilities. Data-driven methods have enhanced traditional approaches to the earth sciences, giving us new insight into a wide range of Earth processes. Machine Learning (ML) methods have enabled the analysis of large data volumes in geosciences that would not have been possible otherwise. The ability to integrate big data into scientific workflows and emerging new machine learning methods has opened new avenues of research and can fill in the gaps where traditional approaches fall short.
This session seeks submissions that:
- Present novel data-driven methods being researched that could greatly benefit Earth science
- Demonstrate the application of ML methods, including deep learning, to understand Earth’s subsystems in ways that were not possible with traditional approaches
- Showcase data-driven research methods that can replace or complement traditional methods
Co-Organized:
Index Terms:
0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]
1849 Numerical approximations and analysis [HYDROLOGY]
1910 Data assimilation, integration and fusion [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Jens F Klump, CSIRO Earth Science and Resource Engineering Perth, Perth, WA, Australia
Conveners: Orhun Aydin, ESRI, Redlands, CA, United States, Emily Law, CalTech JPL, Pasadena, CA, United States and Warren T Wood, Naval Research Laboratory, Stennis Space Ctr, MS, United States
Primary Liaison: Jens F Klump, CSIRO Earth Science Resource Engineering, Perth, Australia
Interpreting the Subsurface at High Lithological Resolution by Integrating Information from Well-Log Data and Rock-core Images (490691)
Jina Jeong1, Eungyu Park1, Irina Emelyanova2, Marina Pervukhina2, Seong-Taek Yun3 and Sungwook Choung4, (1)Kyungpook National University, Department of Geology, Daegu, South Korea, (2)CSIRO, Perth, WA, Australia, (3)Korea University, Department of Earth and Environmental Sciences & K-COSEM Research Center, Seoul, South Korea, (4)KBSI Korea Basic Science Institute, Division of Earth and Environmental Sciences, Seoul, Korea, Republic of (South)
Uncover Similarities of Groundwater Dynamics with Machine Learning based Hydrograph Clustering (490693)
Andreas Wunsch, Karlsruhe Institute of Technology, Karlsruhe, Germany, Tanja Liesch, Karlsruhe Institute of Technology, Department of Civil Engineering, Geo and Environmental Sciences, Karlsruhe, Germany and Stefan Broda, BGR Federal Institute for Geosciences and Natural Resources, Hannover, Germany
Deep Learning for the Exploration of Coal Seam Gas Reservoirs (500927)
Abeer Mazher1, Irina Emelyanova1, Marina Pervukhina1, Tauqir Moughal1, Chris Dyt1, David S Warner2, Erik C. Dunlop2, Mohinudeen Faiz3, David Dewhurst1 and Prue E.R. Warner2, (1)CSIRO, Perth, WA, Australia, (2)Deep Coal Technologies Pty Ltd., Adelaide, SA, Australia, (3)CSIRO, Brisbane, QLD, Australia
GIS-based landslide susceptibility mapping around an operating open-pit mine in North Korea (513796)
Jangwon Suh1, Sung-Min Kim2, Yosoon Choi3, MyeongChan Oh4, Aran Yang4, Jae-Joon Song4 and Hyeong-Dong Park4, (1)Kangwon National University, Energy Engineering, Chuncheon, Korea, Republic of (South), (2)Kangwon National University, Energy Engineering, Samcheok, South Korea, (3)Pukyong National University, Busan, Korea, Republic of (South), (4)Seoul National University, Energy Resources Engineering, Seoul, South Korea
Improvement of Spatial Interpolation Accuracy of Daily Maximum Air Temperature Using Stacking Ensemble Technique (515420)
Dongjin Cho1, Cheolhee Yoo1, Jungho Im1, Jaese Lee2 and Yeonsu Lee1, (1)Ulsan National Institute of Science and Technology, Ulsan, South Korea, (2)Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South)
Utilizing Soumi NPP VIIRS Nighttime Lights Data for Socioeconomic Analysis using Advanced Methods: Case Studies from NASA’s Socioeconomic Data and Applications Center (SEDAC) (582920)
Kytt MacManus1, Susana Beatriz Adamo2, Jane Mills3, Dorothee Cleophee Grant4, Gregory Yetman4 and Mairead Milan5, (1)Columbia University of New York, Palisades, NY, United States, (2)Columbia Univ, Palisades, NY, United States, (3)Columbia University of New York, CIESIN, Palisades, NY, United States, (4)Center for International Earth Science Information Network, Palisades, NY, United States, (5)Center for International Earth Science Information Network, Palisades, United States
A Novel Fuzzy Logic Geospatial Approach to Characterize and Predict Geologic Structural Complexity (600615)
Devin Justman, Leidos Research Support Team, National Energy Technology Laboratory Albany, Albany, OR, United States, Christopher Gabriel Creason, Leidos Research Support Team, National Energy Technology Laboratory, Albany, OR, United States, Kelly K Rose, National Energy Technology Laboratory, Albany, OR, United States and Jennifer Bauer, National Energy Technology Laboratory, U.S. Dept. of Energy, Albany, OR, United States
Implementing Spectral and Textual Characteristics of Rocks for Improved Lithological Mapping using Machine Learning Algorithms (606124)
Chandan Kumar1, Snehamoy Chatterjee2, Thomas Oommen1 and Arindam Guha3, (1)Michigan Technological University, Houghton, MI, United States, (2)Michigan Technological University, Geological and Mining Engineering and Sciences, Houghton, MI, United States, (3)National Remote Sensing Center, Hyderabad, India
Multidimensional Feature Explorer for Unbalanced Spatiotemporal Data (611361)
Dongshuang Li1,2, Hong Gao3, Wen Luo3, Zhaoyuan Yu3 and Linwang Yuan3, (1)Yangzhou University, Yangzhou, China, (2)Texas A&M AgriLife Research Center at El Paso, EL Paso, United States, (3)Nanjing Normal University, Nanjing, China
Class Separability of Land Cover Change Events from Multispectral Satellite Image Time-Series (614228)
Srija Chakraborty, Arizona State University, School of Computing, Informatics, and Decision System Engineering, Tempe, AZ, United States, Antonia Papandreou-Suppappola, Arizona State University, School of Electrical, Computer and Energy Engineering, Tempe, AZ, United States and Philip R Christensen, Arizona State University, School of Earth and Space Exploration, Tempe, AZ, United States
