Research Focus Areas
Microseismic monitoring with deep learning
This project is focused on developing an end-to-end microseismic monitoring workflow using deep neural networks. GMG develops and tests deep-learning algorithms to detect and locate induced microearthquakes using, in particular, data from a deep geothermal well simulation. GMG also explores application to vertical component seismic surveys.
Application of distributed acoustic sensing to monitor microseismicity and velocity changes in the subsurface
Distributed acoustic sensing (DAS) is a sensor technology that is gaining momentum in the oil and gas industry, especially in monitoring microseismicity and seismic velocity changes induced by stress and fluid during fluid injection or extraction. The first objective of this project is to use the Pasadena, California, DAS array as a platform to research how to deal with the challenges of using DAS data for monitoring.
Experimental investigation of the interaction between fluids and failure of rock faults in shear
Fluids are known to trigger a range of slip events spanning from a slow, creeping motion to dynamic earthquake rupture growth. GMG studies the interaction of fluids and faulting in a highly instrumented experimental setup capable of injecting fluid at various rates onto a 3D polymethyl methacrylate specimen's interface, mimicking a fault in the Earth's crust.
Understanding conditions for stable/unstable fault slip induced by fluid injections
When fluids enter an existing fault, they increase pore pressure and promote slip; however, to predict whether the resulting slip would be seismic or aseismic, and how far induced earthquakes might reach, GMG needs detailed understanding of several mechanisms: the effect of pore pressure on fault stability; the effect of dilatancy and compaction on pore fluid; and changes in friction properties due to the presence of fluids. GMG develops numerical models to simulate and assess these effects.
Modeling deformation and seismicity due to fluid injections
Pumping fluids in the subsurface produces deformation due to poroelastic and thermal effects. It can induce seismicity and affects transport properties of the medium. GMG aims to forecast and eventually control these effects for various applications (e.g., geothermal energy production, CO2 storage). GMG therefore develops new approaches to estimate fluids pressure, deformation, transport properties and forecast induced seismicity through a combination of data analysis, thermo-poroelastic stress modeling and reservoir modeling.
Infrastructure system resiliency via InSAR ground deformation monitoring.
Landslides pose a major hazard to local communities and infrastructure systems. Synthetic aperture radar (SAR) imagery, which is now acquired on a weekly basis with a near global coverage, can provide high-spatial resolution measurements of ground surface motion. GMG's goal is to assess how SAR images be used in combination with conventional techniques to monitor ground deformation at millimeter-level precision/accuracy and help forecast landslides.