Timezone: »

Laura Pyrak-Nolte
Laura Pyrak-Nolte

Fri Dec 07 08:15 AM -- 08:15 AM (PST) @

Data Challenge: Machine Learning for Earthquake Detection and Rupture Timing

Laura Pyrak-Nolte, Richard Baraniuk, Greg Beroza, Maarten de Hoop, Brad Hager, Eugene Ilton, Paul Johnson, Steve Laubach, Alan Levander, Semechah Lui, Joe Morris, Beatrice Rivera, James Rustad

Affiliations: Purdue University, Rice University, Stanford University, MIT, PNNL, LANL, Bureau of Economic Geology, University of Toronto, LLNL, Department of Energy-Basic Energy Sciences

Major breakthroughs and discoveries in geophysics are anticipated because of increases in computational power, massive sensor deployments that yield massive datasets, and advancements in machine learning algorithms. However, owing to the inherent complexity, machine learning methods are prone to misapplication, lack of transparency, and often do not attempt to produce interpretable models. Moreover, due to the flexibility in specifying machine learning models, results are often insufficiently documented in research articles, hindering both reliable assessment of model validity and consistent interpretation of model outputs. By providing documented datasets and challenging teams to apply fully documented workflows for machine learning approaches, we expect to accelerate progress in the application of data science to longstanding research issues in geophysics.

In this poster presentation, the guidelines for a challenge problem will be given. Challenge 1 will address the physics of rupture and timing of earthquakes (from laboratory data collected during shearing of gouge-filled faults). While using the data set in the challenge, the expected reported information pertains to supervised and unsupervised machine learning components:
● the architecture of the machine-learning approach and why it was chosen;
● the loss function and learning rule;
● preprocessing designed and applied as appropriate;
● description of and choice of the set of hyperparameters;
● description of featurization or feature learning.
We expect the design of the machine learning approach to be an iterative process and seek a description of this. In view of the lack of ground truth data in general, while being a physics-based challenge, we invite proposed metrics to validate and compare the performance of the different results. Information will be provided on how to obtain the data, timelines for completion of the challenge, and reporting of results.

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

Author Information

Laura Pyrak-Nolte (Purdue University)

Dr. Laura J. Pyrak-Nolte is a Distinguished Professor of Physics & Astronomy, in the College of Science, at Purdue University. She holds courtesy appointments in the Lyle School of Civil Engineering and in the Department of Earth, Atmospheric and Planetary Sciences, also in the College of Science. Dr. Pyrak-Nolte holds a B.S. in Engineering Science from the State University of New York at Buffalo, an M.S. in Geophysics from Virginia Polytechnic Institute and State University, and a Ph.D. in Materials Science and Mineral Engineering from the University of California at Berkeley where she studied with Dr. Neville G. W. Cook. Her interests include applied geophysics, experimental and theoretical seismic wave propagation, laboratory rock mechanics, micro-fluidics, particle swarms, and fluid flow through Earth materials. In 1995, Dr. Pyrak-Nolte received the Schlumberger Lecture Award from the International Society of Rock Mechanics. In 2013, she was made a Fellow of the American Rock Mechanics Association (ARMA). Currently she the President of the American Rock Mechanics Association, and president-elect of the International Society for Porous Media

More from the Same Authors