Primary Expertise
Coordinate and perform large-scale physical and virtual monitoring experiments of buried and aboveground explosions
- Exploit new technologies to develop next-generation capabilities that include Distributed Acoustic Sensing (DAS), machine learning and artificial intelligence plus virtual containers.
- Measure and quantify impacts of climate change on stratospheric sound propagation, hydroacoustic propagation in the Arctic, and generation of methane-sourced gas emission craters in the Russian Arctic.
- Expertise and sensing capability to fuse multi-domain, geophysical observations of explosion sources and their background emissions.
- Provide cutting edge, quantitative uncertainty analysis of explosion source parameters via a toolbox of empirical and mathematical techniques.
Featured Research
- Produce accurate, empirical estimates of explosion yields from scattered seismic waveform coda.
- Quantify and greatly reduce the uncertainties in moment tensors (MTUQ), using seismic observations collected at regional distances from underground nuclear test sites.
- Predict 3-D ray-paths from both stationary and moving infrasound sources over the whole atmospheric column (infraGA).
- Leverage Bayesian methods to produce joint, seismo-acoustic locations from near surface explosion sources.
- Use graph-theory methods to quantify false association and event building errors in seismic networks.
- Combine multiple Rayleigh wave polarization measurement techniques to characterize explosion sources in very complex tectonic environments.
- Empower big data techniques to predict high-fidelity maps of seismic attenuation over the entire globe.
- Demonstrate probabilistic methods to predictively screen small earthquakes from explosions at local distances with seismic phase ratios.
- Develop novel data stream fusion methods to integrate evidence of explosions from multiple waveform and optical modalities, for both signal detection and source identification.
- Advance generalized hypothesis testing techniques to adaptively detect and characterize noisy threat sources in challenging signal environments.
- Characterize climate-induced variability in both infrasonic and hydroacoustic signal environments to quantify propagation uncertainties.
- Development of adaptive, eigen-processing techniques to detect signals and quantify performance of IMS arrays in near-real time.