Analyst | Visionary | Disruptor
An Unconventional Professor
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
My research team
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 and earthquake coupling. We are now extending these methods to analyses of underground energy storage and the detection and characterization of underground facilities. Colleagues on my 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. Some topics include:
Detecting and characterizing undergroun facilities.
Megathrust seafloor deformation and tsunami genesis.
Volcano deformation from satellite imagery.
Detecting creep and failure of underground salt caverns.
Fluid injection and induced seismicity.
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.
Finite Element Models 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. My industry partners and I presented our paper that uses Machine Learning to locate creeping salt in underground salt dome caverns at the SMRI 2023.
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, Underground Storage, 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. My industry partners and I presented a study of seismic hazards and energy solutions for potential underground energy storage options in Pakistan SMRI 2024.
Dr. Tim Masterlark | The Distinguished Professor