The Carl Heiland Lecture Series takes place on Wednesdays at 4:00 PM during the fall and spring semesters. Each week, we are joined by a distinguished speaker from academia, industry, or government on a topic pertinent to the geosciences. The lecture series is a public event open to all members of the Mines community and beyond.
This fall, some Heiland lectures will be offered in person on campus in CTLM 102 and others will only be offered virtually via Zoom. A Zoom link will be available for all presentations so that you can attend from wherever you are in the world. Locations are indicated in the Fall 2021 schedule below.
January 19, 2022
Geophysical detection of abandoned mines and tunnels: Easy on paper … challenging in the field
US Army Engineering Research and Development Center (ERDC)
Detecting subsurface voids such as abandoned mines, tunnels, or dissolution features has proven to be historically difficult using a variety of geophysical methods. What should be a straightforward target on paper—detecting an anomaly with often vastly different physical properties than its surroundings— is a much more difficult task than models and practical application suggest. Results from a variety of near-surface seismic methods, including refraction tomography, backscattered surface waves, diffracted body waves, and full waveform inversion, used to detect relatively small-diameter air-filled voids will be presented.
Dr. Steve Sloan is a Senior Research Geophysicist at the U.S. Army Engineer Research and Development Center located in Vicksburg, Mississippi. He specializes in near-surface seismology, including high-resolution shallow seismic reflection, refraction tomography, and surface wave methods. His research has focused on the application of geophysical methods to defense problems, including clandestine tunnel detection, counter improvised explosive device (C-IED) applications, and geophysical characterization of the shallow subsurface in austere environments around the world. Steve received a B.S. in geology from Millsaps College, and a M.S. and Ph.D. with Honors in geophysics from The University of Kansas. He has authored or co-authored over 100 technical publications related to near-surface geophysics and has received multiple awards for research and development and operational support. He recently served on the Editorial Board of The Leading Edge and is a former Chair of the Near Surface Technical Section of the Society of Exploration Geophysicists.
January 26, 2022
No Heiland this week
February 2, 2022
Geophysical data, rock physics, and geostatistics: how to quantitatively image a sustainable subsurface
University of Wyoming
The goal of subsurface geophysical characterization is understanding and improving the petrophysical description of the subsurface in terms of rock and fluid properties using geophysical data, rock physics models, and statistical methods. The talk includes three different applications that also represent three different phases of Dr. Grana’s career as a geoscientist: seismic characterization for oil and gas reservoirs; seismic monitoring for carbon dioxide sequestration and storage; and seismic and electromagnetic characterization of the critical zone. The common theme of these three applications is the use of geophysical data, the definition of rock physics models to link the measurements to the properties of interest, and the implementation of statistical methods to predict the properties of interest and their uncertainty. The estimation of rock and fluid properties from geophysical data is formulated as a mathematical inverse problem. The Bayesian approach to inverse problems provides the posterior distribution of rock and fluid properties given the measured geophysical data. This approach allows improving the subsurface characterization and quantifying the model uncertainty to make informed decisions for the management of energy and natural resources.
Dr. Dario Grana is an associate professor in the Department of Geology and Geophysics at the University of Wyoming. He is originally from Italy, where he was a first generation student who was lucky enough to have access to free education. His background is as a mathematician, but he accidentally became a geophysicist out of the fear of not being good enough as a mathematician. He always wanted to work in academia, but he did not always feel he could make it. He worked four years at Eni Exploration and Production in Milan, then received a Ph.D. in Geophysics at Stanford University in 2013, and joined the University of Wyoming in the same year. Since then he spent most of his time playing with Matlab, trying to figure out how to be a good professor, and skiing. He is happy with his career so far and he is aware that it would not have been possible without the great mentors he met throughout his life. He supports diversity, equity, and inclusion efforts and believes it all starts from being good human beings. His main research interests are rock physics, seismic reservoir characterization, geostatistics, data-assimilation, and inverse problems for subsurface modeling, but mostly playing with numbers and indices of math equations.
February 9, 2022
Efficient Training of Infinite-Depth Neural Networks via Jacobian-Free Backpropagation
Samy Wu Fung
Colorado School of Mines
A promising trend in deep learning replaces fixed depth models by approximations of the limit as network depth approaches infinity. This approach uses a portion of network weights to prescribe behavior by defining a limit condition. This makes network depth implicit, varying based on the provided data and an error tolerance. Moreover, existing implicit models can be implemented and trained with fixed memory costs in exchange for additional computational costs. In particular, backpropagation through implicit networks requires solving a Jacobian-based equation arising from the implicit function theorem. We propose a new Jacobian-free backpropagation (JFB) scheme that circumvents the need to solve Jacobian-based equations while maintaining fixed memory costs. This makes implicit depth models much cheaper to train and easy to implement. Numerical experiments on classification are provided.
Dr. Fung is an assistant professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He received his Ph.D. degree in applied mathematics from Emory University, Atlanta, GA, USA, in 2019. Prior to joining Mines, he was an assistant adjunct professor in the Department of Mathematics at UCLA. His research interests lie in the fields of inverse problems, optimization, deep learning, and optimal control.
February 16, 2022
Integrating induced seismicity with fault interpretation at the Illinois Basin – Decatur Project
Illinois State Geological Survey
February 23, 2022
March 2, 2022
March 9, 2022
U.S. Geological Survey
March 16, 2022
March 30, 2022
April 6, 2022
Cascades Volcanology Center
April 13, 2022
April 20, 2022
University of Texas
April 27, 2022
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