Signals Machine Learning for Geophysical & Geochemical SignalsNIPS

December 7, 2018
NIPS 2018 Palais des Congres de Montreal, Montreal CANADA

Workshop Details

Major breakthroughs in bridging the knowledge gaps in geophysical sensing are anticipated as more researchers turn to machine learning (ML) techniques; however, owing to the inherent complexity of machine learning methods, they are prone to misapplication, may produce uninterpretable models, and are often insuciently documented. This combination of attributes hinders both reliable assessment of model validity and consistent interpretation of model outputs. By providing documented datasets and challenging teams to apply fully documented workows for ML approaches, we expect to accelerate progress in the application of data science to longstanding research issues in geophysics.

The goals of this workshop are to:

(1) bring together experts from different fields of ML and geophysics to explore the use of ML techniques related to the identication of the physics contained in geophysical and chemical signals, as well as from images of geologic materials (minerals, fracture patterns, etc.);

(2) announce a set of geophysics machine learning challenges to the community that address earthquake detection and the physics of rupture and the timing of earthquakes.

Important Dates

September 4, 2018     Abstract Submission Opens
September 27, 2018     Abstract Submission Closed
Late on September 30, 2018     Notification of Acceptance
December 7, 2018     Workshop Day at NIPS
January 10, 2019      Kaggle Competition Opens

Acknowledgment:  US Department of Energy, Office of Basic Energy Sciences, Chemical Sciences, Geosciences and Biosciences Division.

Geophysical Geochemical Signals