Timezone: »

Machine Learning for Geophysical & Geochemical Signals
Laura Pyrak-Nolte · James Rustad · Richard Baraniuk

Fri Dec 07 05:00 AM -- 03:30 PM (PST) @ Room 515
Event URL: http://www.physics.purdue.edu/MLGGS »

The interpretation of Earth's subsurface evolution from full waveform analysis requires a method to identify the key signal components related to the evolution in physical properties from changes in stress, fluids, geochemical interactions and other natural and anthropogenic processes. The analysis of seismic waves and other geophysical/geochemical signals remains for the most part a tedious task that geoscientists may perform by visual inspection of the available seismograms. The complexity and noisy nature of a broad array of geoscience signals combined with sparse and irregular sampling make this analysis difficult and imprecise. In addition, many signal components are ignored in tomographic imaging and continuous signal analysis that may prevent discovery of previously unrevealed signals that may point to new physics.

Ideally a detailed interpretation of the geometric contents of these data sets would provide valuable prior information for the solution of corresponding inverse problems. This unsatisfactory state of affairs is indicative of a lack of effective and robust algorithms for the computational parsing and interpretation of seismograms (and other geoscience data sets). Indeed, the limited frequency content, strong nonlinearity, temporally scattered nature of these signals make their analysis with standard signal processing techniques difficult and insufficient.

Once important seismic phases are identified, the next challenge is determining the link between a remotely-measured geophysical response and a characteristic property (or properties) of the fractures and fracture system. While a strong laboratory-based foundation has established a link between the mechanical properties of simple fracture systems (i.e. single fractures, parallel sets of fractures) and elastic wave scattering, bridging to the field scale faces additional complexity and a range of length scales that cannot be achieved from laboratory insight alone. This fundamental knowledge gap at the critical scale for long-term monitoring and risk assessment can only be narrowed or closed with the development of appropriate mathematical and numerical representations at each scale and across scales using multiphysics models that traverse spatial and temporal scales.

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 insufficiently 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 workflows 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 identification of the physics contained in geophysical and chemical signals, as well as from images of geologic materials (minerals, fracture patterns, etc.); and
(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.

Target Audience
We aim to elicit new connections among these diverse fields, identify novel tools and models that can be transferred from one to the other, and explore novel ML applications that will benefit from ML algorithms paradigm. We believe that a successful workshop will lead to new research directions in a variety of areas and will also inspire the development of novel theories and tools.

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

James Rustad (University of California Davis)

Jim received his Ph.D. in Geophysics form the University of Minnesota in 1992. He worked as a research scientist at Pacific Northwest National Laboratory (1992-2003), professor at the University of California, Davis (2003-2010), and research associate at Corning Incorporated (2010-2015). Currently retired from the University of California, his ongoing research focuses on aqueous interfacial chemistry, isotope geochemistry, and earth materials.

Richard Baraniuk (Rice University)

More from the Same Authors