Thread: AGU session: S015 - Extracting Information from Geophysical Signals with Machine Learning

Started: 2019-07-05 16:41:20
Last activity: 2019-07-05 16:41:20
Topics: AGU Meetings
Dear Colleagues,

We would like to draw your attention to the following technical session
in AGU 2019 (San Francisco, CA, Dec 09-13). Please consider submitting
an abstract (deadline July 31). We look forward to your participation!

S015 - Extracting Information from Geophysical Signals with Machine Learning

Recent advances in machine learning show great promise in geophysical
applications, which include earthquake detection, phase picking, source
characterization, ground motion prediction, signal denoising, and
subsurface imaging. Future breakthroughs in geophysics should be enabled
as more researchers take advantage of the full spectrum of capabilities
that machine learning and other data science techniques have to offer.
Owing to the inherent complexity of machine learning methods, they are
prone to misapplication, may produce uninterpretable models, and can
easily be insufficiently documented. This combination of attributes
hinders reliable assessment of model validity and consistent
interpretation of model outputs. This session aims to engage geophysical
communities to share their experience with machine learning, to address
the needs (e.g., well documented datasets), and to accelerate progress
in the application of data science to geophysics. We welcome
contributions that are related to applying machine learning techniques
to across the full range of geophysical problems.

Ting Chen
Laura Pyrak-Nolte
Paul Johnson
Gregory Beroza

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