Analyst | Visionary | Disruptor

An Unconventional Professor

Keywords: Big Data | Digital Twins | Earthquakes | Fieldcraft | Finite Element Models (FEMs) | Geomechanical Models | Geologic Hazards | Geothermal Systems | Hydrofracking | Induced Seismicity | InSAR | Machine Learning | Salt Caverns | Solution Mining | Poroelastic | Tsunamis | Underground Storage | Volcanoes

For prospective graduate students

Join me on the journey to explore the Earth


From satellites to submersibles, Earth observation data streams continue to get bigger and outpace our ability to analyze them. My research team is uniquely focused on developing sophisticated finite element models and machine learning algorithms to analyze big data streams for earthquake and volcano deformation. We are now extending these methods to analyses of underground storage of energy resources, carbon sequestration, and induced seismicity. Students working on my research team can expect a world-class research experience, my personal attention and commitment to excellence, and access to my extensive professional network spanning global universities and national agencies, such as the USGS and NASA. Potential projects include:


This link will take you to information regarding the Department of Geology and Geological Engineering's MS and PhD program details. If you have any questions, you can also contact me directly via email at masterlark@sdsmt.edu.


Already have an advisor? Great. But if your advisor does not inspire or is disengaged. Maybe you need a new advisor.

Geodetic information 

An explosion of Earth observation data streams


During the past few decades, advances in GPS and InSAR produced quantum leaps in our ability to observe how earthquakes and volcanoes change the shape of the Earth's surface. Models are the critical linkage between this observed deformation and the inaccessible processes of earthquakes and volcanoes at depth.  The Pinned Mesh Perturbation method revolutionized our ability to combine the power of FEMs with multiple data streams. FEMs are the key to understanding geodetic information. Check out out summary chapters of FEM applications for Earthquakes and Volcanoes.

Digital Twins, Artificial Intelligence, and Machine Learning: 

Optimizing models and understanding the uncertainties


For a given deformation model, we can use forward models to predict the resulting surface deformation. In practice, we face the much more challenging inverse modeling problem of quantifying the deformation source parameters, based on observed deformation.  In both cases, the model configuration defines the relationship between the deformation and its source. Check out our FEM-based Machine Learning application to Okmok Volcano published in JGR. We will present our paper that uses Machine Learning to locate creeping salt in underground salt dome caverns at the upcoming SMRI Spring 2023 Conference.

Volcanoes, Underground excavations, and Salt caverns

Simulating geodetic signatures of pressurized cavities. The predicted surface deformation is a nonlinear function of the location and shape parameters of depressurizing magma chambers, excavation cavities, or creeping salt caverns. Check out our pioneering FEM-based non-linear inverse analysis of Okmok Volcano published in JGR.

Earthquakes and Tsunamis

The near-trench slip configuration and distribution of surrounding rock properties strongly control seafloor deformation and tsunami genesis. Check out our FEM-based analysis of the Tohoku Earthquake and Tsunami published in PAGEOPH.

Poroelasticity, Dike propagation, Hydrofracking, Geothermal Systems, and Induced Seismicity

Fluid-solid (and sometimes thermal) coupling in the crust.


An understanding of the coupling of fluids and solids in the crust is prerequisite to understanding deformation of the crust. My research team develops quantitative representations of the complex fluid-solid interplay poroelastic deformation, dike propagation, hydrofracking, geothermal systems, and induced seismicity. PPT

Dr. Tim Masterlark | Mickelson Professor and Distinguished Professor