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2nd Machine Learning in Solid Earth Geoscience
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 **IMPORTANT UPDATE REGARDING THE GOVERNMENT SHUTDOWN - LANL has not been affected by the government shutdown. If you are an attendee who wishes to participate in the conference but are affected by the government shutdown, please register and pay for the conference. If for any reason you do not receive approval to attend the conference, or need to cancel for any reason, we will refund you 100% of the fee. If you need additional special accommodation because of the government shutdown, please email us directly at cnls-conferences@lanl.gov and we will do everything we can to work with you.

Summary

Machine learning (ML) in its current form is relatively new to geoscience.  In the past, ML was applied to a number of geoscience problems but the number of applications before about 2010 was modest. These applications of ML did not reach their full potential for three primary reasons: scarcity of sufficient data for training and testing,  the inability to handle available data efficiently, and computers with insufficient computational speed to process large data sets. Earthquake catalogs, for example, were analyzed, rather than continuous waveform data, due to limitations in data storage capability as well as limitations in instrument density. For subsurface flow and transport predictions, the paucity of data was due to expensive instrumentation and measurement procedures, insufficiently resolved models etc. As we approach the exascale computational era, modeling capabilities have advanced significantly, allowing us to explore parameter and model spaces more thoroughly. We are now at the confluence of the ability to handle massive data streams ‘big data’, ultra fast and massive computers, significant increases in instrumentation density and quality, and advances in ML. As a result we are just now seeing the beginning of the new era in ML applied to geoscience problems that is marked by analyzing continuous geoscience data streams.

This Center for Nonlinear Studies conference will focus on modern applications of ML to solid earth geoscience problems, including earthquakes (both tectonic and induced), faulting, Earth imaging with a multitude of data types including seismic, gravity, electrical methods, geodesy, state of stress, etc.  We will also explore topics related to geological characterization,  subsurface flow and transport, etc. We limit the conference to the solid earth and exclude atmospheric, space and ocean science. This meeting will bring together leading experts with the goal of identifying synergies between these emerging research areas and identifying common themes and open questions.

Important Dates

Abstract Deadline - January 18, 2019 Extended to January 31, 2019

Hotel Deadline - February 18, 2019

Registration Deadline - February 28, 2019
*Registration is limited and may close sooner if capacity is reached.

Organizing Committee

Steering Committee:

  • Paul Johnson (LANL)
  • Greg Beroza (Stanford)
  • Eric Bylaska (PNNL)
  • Maarten de Hoop (Rice)
  • Youzuo Lin (LANL)
  • Hari S. Viswanathan (LANL)

Organizing Committee:

  • Kipton Barros (LANL)
  • Greg Beroza (Stanford)
  • Harsha Bhat (ENS, Paris)
  • George Guthrie (LANL)
  • Maarten de Hoop (Rice)
  • Aric Hagberg (LANL)
  • Claudia Hulbert (ENS, Paris) 
  • Jeffrey Hyman (LANL)  
  • Paul Johnson (LANL)
  • Satish Karra (LANL)
  • Youzuo Lin (LANL)
  • Maruti Mudunuru (LANL)
  • Daniel O'Malley (LANL)
  • Zhigang Peng (Georgia Tech)
  • Piero Poli (Univ. Grenoble)
  • Chris Ren (LANL)  
  • Bertrand Rouet-LeDuc (LANL)
  • Gowri Srinivasan (LANL)
  • James Theiler (LANL)
  • Daniel Trugman (LANL)
  • Brendt Wohlberg (LANL)

  • When

    Monday, March 18, 2019 - Friday, March 22, 2019

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  • Where

    Inn and Spa at Loretto
    211 Old Santa Fe Trail
    Santa Fe, New Mexico
    USA

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  • Planner

    Jennie Harvey

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  • Websites

    Sponsors
    Center for Nonlinear Studies, Center for Space and Earth Science, Los Alamos National Laboratory

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This event is brought to you by the Center for Nonlinear Studies at Los Alamos National Laboratory.
This event has been declared "Open to the Public - with Registration".

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