Committee on Seismology and Geodynamics - Fall '19Board on Earth Sciences and Resources
Keck Center of the National Academies
500 Fifth St. NW Washington DC 20001
The application of computational algorithms such as neural networks that underpin machine learning (ML) have grown within geophysics over the past several decades. In recent years, the increasing power of computing systems when combined with exponentially growing data holdings is leading to exciting new results and tremendous interest in ML and its application in the geophysical sciences. The solid Earth geosciences have large datasets and are developing the expertise to make major contributions to the ML discipline as a scientific discovery tool.
This meeting reviews progress and future investments needed for a more concerted and long term effort to combine datasets with appropriate data-intensive computing resources. This is the natural laboratory necessary for data and geoscientists to most effectively work together, and COSG will discuss how those workflows can be combined with approaches that provide insights into the physics of earth systems, beyond black-box applications.
Speakers and panelists include:
- Karianne Bergen, Harvard University
- Zachary Ross, California Institute of Technology
- Diego Melgar, University of Oregon
- Qingkai Kong, University of California, Berkeley
- Greg Beroza, Stanford University
- Hannah Kerner, University of Maryland
- Brice Menard, Johns Hopkins University
Register Here for Remote Attendance
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