IN14A:
Deep Learning for Geoscience I


Submit an Abstract to this Session


Session ID#: 60553

Session Description:
Recent advances in computer science and data analytics have brought machine learning techniques to the forefront of geoscience research. As a result, old questions are being addressed in new ways as techniques are being developed to exploit large volume and complex data sets. Among the many machine learning techniques, the deep learning approach has proven to be particularly successful for a variety of scientific and industrial applications. In computer science domain, scientists have recently developed various novel deep learning algorithms for diverse applications. The collection of new algorithms includes convolutional neural networks, generative adversarial networks, and recurrent neural networks, to name a few. Simultaneously, there have been many exciting demonstrations of successful machine learning applications applying deep, active, reinforced, supervised and unsupervised techniques in the geoscience.  The goal of this session is to introduce those recent results and new directions to our geo-community of researchers, educators, and students.
Primary Convener:  Youzuo Lin, Los Alamos National Laboratory, Los Alamos, United States
Conveners:  Emily S Schultz-Fellenz, Los Alamos National Laboratory, EES-14, Los Alamos, NM, United States and David D Coblentz, Los Alamos National Laboratory, Los Alamos, United States
Primary Liaison:  Youzuo Lin, Los Alamos National Laboratory, Los Alamos, United States
Chairs:  Emily S Schultz-Fellenz, Los Alamos National Laboratory, EES-14, Los Alamos, NM, United States, Youzuo Lin, Los Alamos National Laboratory, Los Alamos, United States and David D Coblentz, University of Arizona, Tucson, United States
OSPA Liaison:  Youzuo Lin, Los Alamos National Laboratory, Los Alamos, United States
Index Terms:

0520 Data analysis: algorithms and implementation [COMPUTATIONAL GEOPHYSICS]
0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]
1906 Computational models, algorithms [INFORMATICS]
1914 Data mining [INFORMATICS]

Abstracts Submitted to this Session:

Novelty Detection for Multispectral Planetary Images (361056)
Hannah Rae Kerner, Arizona State University, School of Computing and Augmented Intelligence, Tempe, AZ, United States, Danika F Wellington, Arizona State University, Tempe, United States, Kiri L. Wagstaff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, Samantha Jacob, Arizona State University, Tempe, AZ, United States, Prof. James F Bell III, PhD, Arizona State University, School of Earth and Space Exploration, Tempe, United States and Heni Ben Amor, Arizona State University, School of Computing, Informatics, and Decision Systems Engineering, Tempe, AZ, United States
Evaluating Generative Adversarial Network Stochastic Parameterizations of the Lorenz '96 Model at Climate and Weather Scales (398720)
David John Gagne II, NCAR/MMM, Boulder, United States, Hannah Mary Christensen, University of Oxford, Department of Physics, Oxford, United Kingdom, Aneesh Subramanian, University of Colorado at Boulder, Atmospheric and Oceanic Sciences, Boulder, United States and Adam H Monahan, University of Victoria, Victoria, BC, Canada
Weakly Supervised Deep Learning for Segmentation of Cropland in Remote Sensing Imagery (401977)
Sherrie Wang, Stanford University, Stanford, CA, United States, William Chen, Stanford University, Computer Science, Stanford, CA, United States, George Azzari, Atlas AI, Palo Alto, CA, United States and David B Lobell, Stanford University, Department of Earth System Science & Center on Food Security and the Environment, Stanford, United States
Developing fine-scale snow cover fraction estimates using deep learning (418143)
Soni Yatheendradas, University of Maryland College Park, College Park, MD, United States, Sujay V Kumar, NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, Greenbelt, MD, United States and Daniel Duffy, NASA Center for Climate Simulation, Greenbelt, United States
A novel data fusion model to integrate heterogeneous geo-sensory networks for air quality prediction (437147)
Yawen Zhang1, Duanfeng Gao1, Qin Lv1, Robert Dick2, Michael Hannigan3 and Daven K Henze4, (1)University of Colorado Boulder, Boulder, CO, United States, (2)Madison, AL, United States, (3)University of Colorado at Boulder, Boulder, CO, United States, (4)University of Colorado Boulder, Boulder, United States
Convolutional autoencoders for earthquake anomaly detection in InSAR (442767)
Guillaume Rongier1, Cody Millard Rude1, Thomas Herring1 and Victor Pankratius2, (1)Massachusetts Institute of Technology, Department of Earth, Atmospheric and Planetary Sciences, Cambridge, MA, United States, (2)Massachusetts Institute of Technology, Cambridge, MA, United States
Deep Learning recognizes weather and climate patterns (Invited) (453911)
Karthik Kashinath1, Mr Prabhat2, Mayur Mudigonda3, Ankur Mahesh4, Soo Kyung Kim5, Yunjie Liu6, Samira Kahou7, Benjamin A Toms8, Evan Racah9, Christopher Beckham10, Chris Pal10, Tegan Maharaj10, Jim Biard11, Kenneth Kunkel12, Dean Norman Williams13, Travis Allen O'Brien14, Michael F Wehner15 and William Drew Collins16, (1)NVIDIA Corporation, Santa Clara, CA, United States, (2)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (3)University of California Berkeley, Berkeley, CA, United States, (4)Lawrence Berkeley National Laboratory, Earth and Environmental Science, Berkeley, United States, (5)LLNL, Livermore, United States, (6)UC Davis-Land, Air, Water Rsrc, Davis, CA, United States, (7)Microsoft Research, Montreal, Canada, (8)Colorado State University, Fort Collins, CO, United States, (9)Cooperative Institute for Climate and Satellites, Asheville, NC, United States, (10)U. Montreal, Montreal, Canada, (11)Cooperative Institute for Climate and Satellite North Carolina State, Asheville, NC, United States, (12)University of Illinois at Urbana Champaign, Urbana, United States, (13)Lawrence Livermore National Laboratory, Livermore, CA, United States, (14)Lawrence Berkeley National Lab, Climate & Ecosystem Sciences Division, Berkeley, United States, (15)Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, United States, (16)Berkeley Lab and UC Berkeley, Berkeley, United States
Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models (468594)
Dr. Velimir monty V Vesselinov1, Boian Alexandrov2, Maruti Mudunuru3, Satish Karra1 and Daniel O'Malley4, (1)Los Alamos National Laboratory, Los Alamos, United States, (2)LANL, Santa Fe, NM, United States, (3)Los Alamos National Laboratory, Los Alamos, NM, United States, (4)Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, United States

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