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 spring, some Heiland lectures will be offered in person on campus in Berthoud Hall Room 241 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 Spring 2022 schedule below.

January 19, 2022

Geophysical detection of abandoned mines and tunnels: Easy on paper … challenging in the field

Steve Sloan
US Army Engineering Research and Development Center (ERDC)

Berthoud 241


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.

Speaker bio

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.  



To receive the Zoom link for this presentation contact Ge Jin at or Noelle Vance at



January 26, 2022

No Heiland this week



February 2, 2022

Geophysical data, rock physics, and geostatistics: how to quantitatively image a sustainable subsurface

Dario Grana
University of Wyoming

Zoom –


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.

Speaker bio

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.



A Zoom link will also be available for this presentation. To receive the link contact Ge Jin at or Noelle Vance at

February 9, 2022

Efficient training of infinite-depth neural networks via Jacobian-Free backpropagation

Samy Wu Fung
Colorado School of Mines

Berthoud 241


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.

Speaker bio

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.


If you have questions, please contact Ge Jin at or Noelle Vance at

February 16, 2022

Integrating induced seismicity with fault interpretation at the Illinois Basin – Decatur Project

Sherilyn Williams-Stroud
Illinois State Geological Survey

Zoom Presentation


Monitoring of induced seismicity after injection of super-critical CO2 was used to help determine pathways the fluid took during migration over time and to assess the risk of felt seismicity in the Illinois Basin – Decatur Project (IBDP). The microseismic activity at the site indicates locations where existing fractures and faults were reactivated, but do not have a direct correlation with locations of faults mapped in the seismic reflection volume. Some microseismic events were large enough to determine focal mechanisms, but no injection-related felt seismicity has occurred. Most of the induced seismicity occurs below the reservoir in fractured low porosity/permeability igneous basement rocks where fault identification proved problematic. The reservoir itself, the Cambrian Mt. Simon Sandstone, has high porosity and permeability, with much less common fracturing and faulting. The temporal development of the microseismicity at IBDP indicates stress perturbations that migrated largely to the north and west of the injection location, and which were concentrated in clusters elongated in a NE-SW orientation. Detailed spatial-temporal analysis of the clusters indicates more complex internal structure, suggesting reactivations of planes within a fracture corridor rather than a single large fault plane. We suggest that horizontal fluid migration occurring in the Mt. Simon sandstone could be the dominant pathway for transmission of fluid and pressure away from the injection well to locations that are hydrologically connected to the basement rocks. It is possible that the fluid directional pathway is also accommodated by open fractures in the basement with orientations that connect the NE-SW oriented reactivated faults. We use this integrated data set to create a fault and fracture model that is consistent with the interpretation of the seismic reflection data and the observed microseismicity as a basis on which to test the potential for induced slip on existing fault planes and the risk for induced felt seismicity.

Speaker bio

Sherilyn Williams-Stroud is a research scientist and structural geologist, at the Illinois State Geological Survey/University of Illinois Urbana-Champaign. Her areas of expertise include structural geology and fracture analysis and modeling for geo-energy production, with a specialization in microseismic data interpretation and induced seismicity analysis. She began her career at the U.S. Geological Survey doing research in the Energy Resources Branch and joined the private sector as a senior research scientist with ChevronTexaco, during which time she also taught geology as an adjunct at the University of Houston. She was a full-time faculty member at Whittier College when she joined Midland Valley Exploration as the Geology Team Leader and the Technical Lead for development of their fracture modeling module. After Midland Valley she joined MicroSeismic, Inc., where prior to becoming their chief geologist, she partnered with the chief geophysicist to develop a patented methodology to integrate microseismic data into geologic interpretations and build discrete fracture network models for use in reservoir simulation. As a senior geological advisor with Occidental Oil and Gas she worked on fractured reservoir interpretations from various Oxy assets including the Monterey Formation. In addition to teaching industry short courses, she taught part time at California State U. Los Angeles and Northridge. She served for four years as a member of the National Academies of Sciences Engineering and Medicine (NASEM) Committee on Seismology and Geodynamics, helping to inform federal government stakeholders on issues of importance related to both natural and induced seismicity. Dr. Williams-Stroud received her B.A. in geology from Oberlin College, and her M.A. and Ph.D., from The Johns Hopkins University. She is a licensed professional geologist in California.

To access this speaker via Zoom, please use the following without spaces : https:// mines. /j/97650641229

February 23, 2022

Earth’s bulk structure and heterogeneity from big data and full-spectrum tomography 

Pritwiraj Moulik
Princeton University
Berthoud 241


Reconciliation of diverse techniques and big data across traditionally siloed disciplines has emerged as a frontier area for Earth exploration. Future challenges include: (1) Leveraging both legacy and evolving community expertise towards harnessing the burgeoning geophysical data, and (2) Modeling physical properties in a way that facilitates self-consistent inferences between geodynamics, geochemistry, seismology and mineral physics. This talk discusses the four prongs – data, theory, model, and community – of the three-dimensional reference Earth model (REM3D) project, and recent findings on interior structure. Progress in modeling the Earth’s interior is driven by diverse data, ranging from astronomic-geodetic constraints to full seismic waveforms and derived measurements of body waves (~1–20s), surface waves (~20–300s) and normal modes (~250–3000s). Full-spectrum tomography employs these observations to constrain physical properties – seismic velocity, anisotropy, density, attenuation and the topography of discontinuities – in variable resolution. Reconciliation of data from research groups worldwide involves retrieving the missing metadata, archiving in scalable storage formats, documenting outliers indicative of the limitations in some techniques, and quantifying summary reference data with uncertainties.

Robust thermo-chemical inferences on heterogeneity require self-consistent descriptions of bulk properties and lateral variations as constrained by the reference datasets. A new radial (1D) reference model (NREM1D) is constructed to account for nonlinear effects from the strong crustal variations, and geographic bias in sampling heterogeneity. The relative variation (3D) of shear velocity, density and compressional velocity disfavors a purely thermal contribution to heterogeneity in the lowermost mantle, with implications for the long-term stability and evolution of superplumes.

We conclude with an infrastructure for creating multiscale models that uses advancements in inverse theory, machine learning, data processing and wave propagation. By coupling existing, reconciled observations with predictions for arbitrary locations, our methods will be useful for identifying regions of scientific interest, validating new techniques, planning future seismic deployments, and testing hypotheses about the Earth’s interior.

Speaker Bio

Dr. Pritwiraj (Raj) Moulik works on planetary-scale geophysical problems using techniques that reconcile and fit concurrently a wide variety of geophysical observations. He leads a team of 40+ researchers across more than seven countries to create a three-dimensional reference Earth model, while formulating new algorithms using Full Spectrum Tomography. Geological motivations include evaluating radial anisotropy with implications for mantle flow, detecting seismological signatures of chemical heterogeneity, and radial models to represent bulk properties. Raj also works with collaborators across disciplines to infer the modeled properties in terms of the current state and evolution of the Earth’s interior. He holds a PhD in Earth and Environmental Sciences from the Lamont-Doherty Earth Observatory of Columbia University, and is currently an associate research scholar at Princeton University.

March 2, 2022

There will be no Heiland Lecture this week as many people will be attending the SEG Distinguished Instructor Short Course “Forensic Data Processing with Joe Dellinger”, from 8:00 a.m. to 5:00 p.m. in 213 McNeil Hall.

More information about the short course and registration information are available at the following links:

Course Description



March 9, 2022

The Future of Earthquake Impact Estimation*

David Wald
U.S. Geological Survey

In person: Berthoud 241


Estimating impacts due to earthquakes—whether rapidly for emerging disasters or planning for future scenarios—entails the direct interface of seismological and civil engineering expertise and tools. Both endeavors require considering uncertain models and data since the main components of loss estimation—namely shaking, exposure, and vulnerabilities—entail inherent uncertainties. Since actionable response or planning requires confidence in our results, improvements in our loss calculations require continued collaboration. Fortunately, advancements in remote sensing, rapid in-situ monitoring and impact reporting, and machine learning allow for innovative data-fusion strategies that integrate with existing models and should significantly improve the accuracy and spatial resolution of rapid shaking and loss estimates. Some key contributing datasets, when integrated, could radically improve our loss estimate capabilities include better ShakeMap macroseismic constraints, global building footprints and inventories, Bayesian fatality updating based on early reporting, Structural Heath Monitoring, and several other emerging technologies. Some of these same tools and strategies are also applicable for long-term loss and risk assessments. Wald’s 2021 William B. Joyner lecture will feature a combined seismological and earthquake engineering view of future earthquake response and recovery, where the initial impact estimates —as well as secondary hazards—are rapidly supplemented with crowd-sourced and remotely sensed observations that are integrated holistically for more a more accurate view of the consequences.

* “Practice Talk ”for upcoming William B. Joyner Lectureship awarded by the Seismological Society of America and the Earthquake Engineering Research Institute.

Speaker bio

David J. Wald is involved in research, development, and operations of real-time information systems at the NEIC. He is responsible for developing and managing ShakeMap, which provides near-real-time maps of ground motion and shaking intensity following significant earthquakes; the citizen-science earthquake reporting system Did You Feel it?; and leads development and operations of other systems for post-earthquake response and pre-earthquake mitigation, including ShakeCast, Ground Failure, and PAGER.

Along with his work at USGS, Wald is the Editor-in-Chief of EERI’s premier journal Earthquake Spectra and is an Adjunct Professor in the Geophysics Department at the Colorado School of Mines. Wald was an IRIS/SSA Distinguished Lecturer in 2004 and received EERI’s Distinguished Lecturer award in 2014. He also served on the Board of Directors for SSA and EERI and was the 2009 recipient of SSA’s Frank Press Public Service Award. In 2021, Wald received the USGS Shoemaker Lifetime Achievement Award in Communications, an award granted annually to a scientist who creates excitement and enthusiasm for science among non-scientists by using effective communication skills.




March 16, 2022


Carbon sequestration in the subsurface: the atlas and the monitoring  

Yunyue Li
Purdue University

In person: Berthoud 241


As the global population continues to grow and societies pursue economic prosperity for their citizens, the demand for access to affordable energy has never been greater to improve quality of life, to achieve greater life expectancy, to reduce poverty, and to provide higher levels of education. While energy needs in OECD nations have seen an overall decreasing trend in recent years thanks to the improved energy efficiency and the conscious efforts to mitigate the risks of climate change, energy demands in the developing regions are still rapidly increasing. To address the dual challenge of satisfying the growing energy demand while reducing greenhouse gas (GHG) emissions, it is important to acknowledge the necessity of all sources of energy and the utmost importance of decarbonization. Carbon capture and sequestration (CCS) in the subsurface is considered one of the most important decarbonization technologies to achieve societal climate goals because of its capability to decarbonize some of the most carbon-intensive industries. In this talk, we first focus on the Southeast Asia region to evaluate its carbon storage capability in the subsurface. We map the potential storage sites among the depleted oil and gas reservoirs and estimate the total storage volume at the reservoir and the basin level. We then demonstrate a machine-learning-based approach to monitor presence and migration of super-critical CO2 in the subsurface once it is injected at the storage site. We emphasize the importance of intergovernmental collaboration and technological exchange to achieve the 1.5 oC goal that is crucial for the survival of humanity.

Speaker bio

Dr. Yunyue Elita Li joined the Department of Earth, Atmospheric, and Planetary Science at Purdue University as a Mary J. Elmore New Frontiers Associate Professor in Data Science in August 2021. Prior to that, she worked in the Department of Civil and Environmental Engineering at the National University of Singapore as an assistant professor. Elita did her postdoctoral research at Massachusetts Institute of Technology, holding a joint position in the Earth Resources Laboratory and the Department of Mathematics. She received her Ph.D. and M.S. degrees in Geophysics from Stanford University in 2014 and 2010, respectively. She obtained her B.S. degree in Information and Computational Science from China University of Petroleum, Beijing in 2008. Elita’s research group works on geophysical applications in urban environments for smart cities and sustainable developments. By integrating geophysical inversion techniques, ambient noise data analysis, and distributed sensor networks, her group focuses their research efforts on the development of noninvasive, high-resolution, and real-time systems to solve pressing challenges in space, water, security, and sustainability. Elita was the recipient of the J. Clarence Karcher Award from SEG in 2018 and nominated as the SEG South & East Asia Honorary Lecturer for 2022 


March 30, 2022

Toward real-time estimation and forecasting in hydrogeophysics

Fred Day-Lewis

Berthoud 241
Reception in the Marquez Atrium from 5:00 to 6:00 p.m.


Geophysical methods are increasingly used to monitor subsurface processes, providing valuable information to qualitatively constrain or quantitatively calibrate predictive models for flow, transport, and other physical or chemical processes. In groundwater hydrology, the dominant paradigm for model calibration entails solutions of deterministic (or stochastic) inverse problems to identify optimal estimates (or realizations) of model parameters. This approach is limited in several respects. First, sources of structural model error (e.g., incomplete physics, errors in boundary conditions, etc.) are commonly discounted, the consequences of which are rarely quantified but possibly important for joint inversion. Second, the conventional approach is poorly suited to real-time (‘online’) estimation or forecasting, as data are analyzed in batch rather than recursively assimilated; that is, the model’s calibration is not amenable to incorporation of new data as it becomes available. In this presentation, some recent examples of Kalman-based filtering and smoothing are reviewed, with applications to recursive estimation for groundwater/surface-water exchange and groundwater recharge. The algorithms discussed are amenable to real-time application for estimation and potentially forecasting. Filtering and smoothing frameworks (e.g., Kalman-based) allow for calibration of mechanistic process-based (‘white-box’) models, or data-driven time-series or transfer function (‘black-box’) models, or hybrid (‘grey-box’) models between these end members. In other fields, such as real-time autonomous navigation, recursive estimation and forecasting are common and used effectively within control systems. We see enormous potential for expanded use of filtering and smoothing in hydrology and hydrogeophysics; however, this will require (1) a reframing of how hydrologists and geophysicists conceive of model calibration, and (2) an understanding that models exist on a continuum between white- and black-box models.

Speaker bio

Dr. Fred Day-Lewis joined PNNL in 2021 as a chief geophysicist in the Environmental Subsurface Science Group within the Earth Systems Science Division. Prior to starting at PNNL, Fred worked for the U.S. Geological Survey for 18 years as a Research Hydrologist. Fred has worked on a variety of applied-research projects related to subsurface characterization and monitoring, groundwater remediation, groundwater/surface-water exchange, geophysical inverse problems, thermal methods, and hydrologic parameter estimation. Fred currently serves as an associate editor for the journal Groundwater. He previously served as an associate editor for Water Resources Research, Geosphere, and Hydrogeology Journal, and he co-edited the American Geophysical Union (AGU) monograph Subsurface Hydrology: Data Integration for Properties and Processes. Fred is a past president of the AGU Near Surface Geophysics Section, and past Vice President (Committees) for the Environmental and Engineering Geophysics Society. He was elected Fellow of the Geological Society of America in 2015 for seminal contributions to hydrogeophysics, and he was elected a Laboratory Fellow at PNNL in 2021.



April 6, 2022

Operations at the Cascades Volcano Observatory: Geophysical Networks, Lahars at Mount Rainier, Jackhammers, and Fissure Eruptions in Hawaii

Rebecca Kramer
Cascades Volcanology Center
BE 241


The USGS Cascades Volcano Observatory (CVO) monitors major volcanic centers throughout Oregon and Washington. In the last decade permanent monitoring networks have expanded rapidly to include seismic and geodetic equipment at six Cascade volcanoes and permanent gas monitoring at Mount St. Helens. The majority of the networks have been upgraded to support real-time transmission of broadband seismic and multi-constellation GNSS data. The challenges of the Pacific Northwest climate necessitate the need for a full-time team working to keep remote monitoring stations operating year round.

In recent years a major focus has been upgrading a lahar detection system that was installed in the late 1990s along high-risk drainages near Mount Rainier. The 14,411′ glacier-clad volcano is unique for its apparent ability to trigger massive lahars that could threaten population centers without any apparent volcanic unrest. The project has presented many interesting challenges including the need for a reliable detection and alarming system, meeting the power and telemetry requirements of modern geophysical equipment, expanding monitoring within Mount Rainier National Park around the edifice, and working toward improved algorithms for flow characterization.

Finally, CVO provides support to other U.S. observatories. This included science, logistical, and field support during the 2018 lower East Rift Zone eruption on Kīlauea in Hawaii. The first weeks of the fissure eruption required rapid response and adaptation, especially in the era of social media.

Speaker bio

Rebecca Kramer graduated from Mines with a B.S. in Geophysical Engineering in 2009. After a brief stint in the Geological Engineering program and working at the NEIC on campus she moved to Socorro, New Mexico to complete an M.S. in Geophysics at New Mexico Tech. In 2012 she spent three weeks in Ecuador, defended her thesis, got married, and upended all plans to move to the Pacific Northwest. While working as a science educator at an aviation and space museum, she started volunteering with the USGS Cascades Volcano Observatory (CVO) in 2013. While volcano infrasound was the focus of her master’s thesis, she discovered her real passion for volcano geodesy at CVO. She relocated to the Big Island of Hawaii for four months in 2014 where she worked on early stages of development and implementation of seismic ambient noise monitoring while supporting an eruption response. She returned to CVO as a part-time employee, eventually being promoted as the network planning and field installation lead for an upgraded Lahar Detection System on the west side of Mount Rainier. She continues to also work as the de facto GNSS network manager at CVO and supports field installations and maintenance at volcanoes throughout Oregon and Washington. In her free time, she enjoys sampling Willamette Valley wine, hosting board game nights, and taking naps with her cat.



April 13, 2022

Toward a Wavescope – AI-driven, Bayesian imaging and monitoring of subwavelength microstructures with finite-frequency waves

Ivan Vasconcelos

BE 241
Reception to be held in GC 200 Outside Bunker Auditorium from 5:00-6:00 p.m.


More often than not, important geologic processes occur at micro-scales, e.g., fluid flow, mineral-phase changes, chemically-induced alteration, rock-frame compaction, or even mechanical ruptures/instabilities leading to large earthquakes. However, reliably imaging material properties at such scales from remote long-wavelength information contained in either seismic or EM fields has long been a challenge to the geophysical, engineering and material science communities. In this talk, we present a general framework for the estimation of sub-wavelength material properties from long-scale waves, building on recent advances on statistical microstructure descriptors (SMDs) within the field of material science.  In geoscience, traditional approaches to describing material microheterogeneity rely on either analytical inclusion-based models, or in sample-based digital rocks: each of these having their pros and cons. Here, we by discussing the role of SMDs and more importantly  the so-called ‘Reconstruction’ problem, to statistically describe microheterogeneous geo-materials in a manner that is capable of generalizing complex geometrical information hidden in microstructures, while also retaining realism and sample fidelity. To these advances in material descriptions with imaging, we rely on wave-equation-based Strong Contrast Expansions (SCEs) to predict frequency/scale-dependent effective wave properties for acoustic, elastic and EM waves.  We briefly discuss how SMD-described microstructures affect long-wave properties – and in particular how they not only predict frequency-dependent attenuation due to sub-wavelength scattering, but that attenuation is particularly sensitive to microstructure when compared to effective wavespeeds. When it comes to the estimation of microstructure properties from wave observations, the problem becomes substantially more difficult because realistic microscale parameters could in principle have far too many degrees of freedom than what is observable from finite-frequency wave data. As such, it is key that any method that aims at realistically retrieving microstructure information from long-scale wave data accounts for uncertainty, while also handling the highly nonlinear nature of microstructure-dependent effective wave properties. To that end, we combine our SMD and SCE approaches for effective wave properties with the supervised machine-learning method of Random Forests to construct a Bayesian approach to infer microstructure properties from effective wave parameters as observables. This method yields full posterior distributions for microstructure parameters (e.g., property contrast, volume fraction, and geometry information) from frequency-dependent observations of wave velocities and attenuation. We present several examples of inference scenarios, showing, for example, that i) attenuation is key to microstructure imaging, and ii) microgeometry information can only be reliably retrieved if either contrast or volume fraction are well known a priori. We illustrate of inference approach with several examples of analytical and real microstructures, including data from a  laboratory compaction experiment controlled by microscale CT imaging. Finally, we close with a discussion on the implications of our current results for new imaging monitoring tools for applications such as carbon/hydrogen storage or geothermal energy production.

Speaker bio

Ivan Vasconcelos has been an associate professor of Applied Geoscience at Utrecht University (UU) since 2016. His research focuses on imaging science primarily in geophysical and rock physics applications at field and laboratory scales, but also with strong multidisciplinary ties to material science, engineering and medical imaging. He received his PhD in Geophysics from the Colorado School of Mines (USA) in 2007, and before re-joining academia worked in industrial research at ION Geophysical and Schlumberger. Author of over 50 peer-reviewed publications and 12 patents, Ivan is recognized as a leading expert in imaging, having received the 2014 SEG J. Clarence Karcher Award for outstanding young scientists, and being honored as SEG Honorary Lecturer in 2018. He has served on the editorial boards of Geophysics (2009-2016), and Geophysical Journal International (2016-2020), and regularly referees across disciplines (e.g, Physical Review journals, Nature Materials, IEEE journals).


April 20, 2022

Adaptive spatial sensitivity functions for rapid modeling and inversion of 3D borehole geophysical measurements

Carlos Torres-Verdin
University of Texas

In-person: Berthoud 241


Numerical modeling of borehole geophysical measurements is CPU intensive, especially along high-angle and horizontal wells. For instance, modeling neutron and density measurements along a 100 m well segment requires one day of CPU time on the University of Texas-Austin’s Stampede cluster using Monte Carlo N-Particle simulation code (MCNP, Los Alamos National Laboratory). Modeling tri-axial electromagnetic and acoustic borehole measurements can take up to three hours of CPU time on the same cluster. These overly taxing computer simulation times prevent the implementation of separate and joint inversion methods needed to accurately interpret multiple borehole measurements into fluid/solid rock constituents of spatially complex rocks; they also limit engineering attempts for real-time 3D subsurface well navigation with reactive control. Nonetheless, most borehole measurements exhibit a spatially localized range of sensitivity (volume of investigation) within the surrounding rocks. Such an important measurement property lends itself to the implementation of quasi-linear approximations of instrument responses with respect to spatial perturbations of rock properties.

We developed the new concept of 3D adaptive spatial sensitivity functions to approximate all existing borehole geophysical measurements without limitations on rock, borehole, or geometrical complexity. The approximation delivers simulation errors smaller than 3-5% in minutes/seconds of CPU time along a 100-m-long well trajectory and is amenable to rapid inversion approaches based on deterministic of Bayesian techniques. This presentation describes the fundamentals of the new approximation and its implementation to simulate resistivity, nuclear, acoustic, magnetic resonance, and formation-tester measurements acquired along wellbore trajectories. It also describes examples of joint inversion methods based on the new approximation and applied to several cases of measurements acquired in spatially complex rocks and high-angle and horizontal wells.

Speaker bio

Carlos Torres-Verdín received his B.Sc. in Engineering Geophysics from the National Polytechnic Institute of Mexico, his M.Sc. in Electrical Engineering from the University of Texas at Austin, and his Ph.D. in Engineering Geoscience from the University of California-Berkeley. He has been a research scientist with Schlumberger-Doll Research and a reservoir specialist and technology champion with YPF (Buenos Aires, Argentina). Since 1999, he has been affiliated with the Department of Petroleum and Geosystems Engineering of the UT-Austin, where he is a professor and holds the Brian James Jennings Memorial Endowed Chair in Petroleum and Geosystems Engineering. He conducts research on borehole geophysics, formation evaluation, petrophysics, well logging, and integrated reservoir description. Dr. Torres-Verdín is the founder and director of the Research Consortium on Formation Evaluation at the University of Texas at Austin, which has been in operation for 21 years and is currently sponsored by 20 companies. He has published over 230 refereed journal papers and over 250 conference papers, two book chapters, co-authored one book, is co-inventor of 6 U.S. patents, has served as Guest Editor for Radio Science, invited Associate Editor for Interpretation (Society of Exploration Geophysicists, SEG), Associate Editor for the Journal of Electromagnetic Waves and Applications, SPE Journal (Society of Petroleum Engineers, SPE), and Petrophysics (Society of Petrophysicists and Well Log Analysts, SPWLA), chair of the editorial board of The Leading Edge (SEG), Editor of Petrophysics (SPWLA) and Assistant Editor for Geophysics (SEG). He is a member of the research committee of the SEG, was a member of the technical committee of the SPWLA during two 3-year terms, was VP of Publications of the SPWLA during two one-year terms, and currently serves as VP Technology of the SPWLA. Dr. Torres-Verdín is recipient of the 2020 Virgil Kauffman Gold Medal from the SEG, 2019 Anthony Lucas Gold Medal from the SPE, 2017 Conrad Schlumberger Award from the EAGE (European Association of Geoscientists and Engineers), 2014 Gold Medal for Technical Achievement from the SPWLA, 2008 Formation Evaluation Award from the SPE, 2006 Distinguished Technical Achievement Award from the SPWLA, Distinguished Member of the SPE, and Honorary Member of the SEG. He also received the 2003, 2004, 2006, and 2007 Best Paper Awards in Petrophysics (SPWLA), Honorable Mention for the 2015 Best Paper published in Geophysics, 2020 Best Paper Award published in Geophysics, 2006 and 2014 Best Presentation Awards and the 2007 Best Poster Award by the SPWLA, and was designated Distinguished Technical Speaker during 2006-2007 and 2013-2014 by the SPWLA. Dr. Torres-Verdín has supervised 34 PhD and 47 Master’s students, conducted numerous industry training courses, co-chaired several technical workshops and conference sessions, and has served as member of multiple SPE, SPWLA, and SEG committees in the past.


April 27, 2022

Near-surface cryo-hydrology from Greenland to Europa: Insights from ice-penetrating radar

Riley Culberg

In-person: Berthoud 241


The Greenland Ice Sheet is currently the second largest contributor to barystatic sea level rise, with mass loss dominated by surface melt processes. However, there is significant uncertainty in the magnitude and timing of future mass loss, in part because some poorly-constrained fraction of annual surface melt remains stored inside the ice sheet, rather than running off into the ocean. This near-surface hydrologic system plays an important role in modulating ice sheet balance, but it is difficult to observe with most field or remote sensing methods and consequently remains poorly understood. I will discuss how ice-penetrating radar can be used to quantitatively characterize the evolving structure of the near-surface from local to ice-sheet scales. In particular, I will show how an instrument-informed approach to data analysis can offer new insights into the interactions between extreme weather, ice dynamics, and the flow, storage, and refreezing of water in the shallow subsurface. Some of these water systems are also compelling analogs for cryo-hydrologic processes elsewhere in our solar system, and I will touch on what the Greenland Ice Sheet can tell us about double ridges on Jupiter’s moon Europa.

Speaker bio

Riley Culberg is a Ph.D. Candidate in the Department of Electrical Engineering at Stanford University and holds an M.S. in Electrical Engineering from Stanford and a B.S. in Computer Science and Geospatial Information Science from the United States Military Academy. He is the recipient of a National Defense Science and Engineering Graduate Fellowship and Stanford’s Diversifying Academia, Recruiting Excellence (DARE) Doctoral Fellowship. His work focuses on the application of ice-penetrating radar to the study of the near-surface hydrology and interior structure of ice sheets and icy planetary bodies. He is particularly interested in the role that shallow water systems play in modulating sea-level contributions from the Greenland Ice Sheet in a warming world.


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