Ambient Noise Seismology We use 100s of TB of ambient seismic noise from seismic stations, cross correlate signals using single and multiple station approaches to get a time-dependent estimate of the Earth response. We use these estimates to predict ground motions for future earthquakes and image the Earth subsurface. We also use their time dependence evaluate the changes in the subsurface due to effects ranging from earthquake damage (short time scales), seasonal climate effects, and long-term hydrological effects.
Observational Earthquake Dynamics We extract the dynamic source effects of large earthquakes in seismograms. We use and develop techniques to harness the information from arrays of seismometers. We explore theoretical and empirical ways to extract 3D Green’s functions. We use kinematic and simple dynamic model to validate theories with seismic observations. We have some focus on subduction-zone earthquakes.
Computational Data Seismology We develop open-source Python and Julia tools for high-performance computing of correlation seismology. We develop data-flows for Cloud-based platforms through native-cloud and webservices approaches. Our tools aim to be portable and simple enough to run on laptops as well as on Cloud and HPC.
Near-surface Seismology We explore the relations between seismic and attenuation spatio-temporal perturbations with atmospheric and geohydrological forcings. We develop proxies using our seismic measurements to groundwater levels.
Machine Learning in Seismology We develop machine learning tools for various applications in Earthquake Sciences: earthquake detection, location, phase picking, wavefield separation, time series forecast, early warning toy systems.