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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.
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Current explosion monitoring relies on approximations that become unreliable for small explosions, such as North Korea’s 2006 declared nuclear test.  Los Alamos’s geophysical explosion monitoring team is developing new methods that incorporate more accurate high-frequency wave simulations, improving detection and characterization of underground nuclear tests.

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Geophysicist Charlotte Rowe performing differential gravity survey at the NNSA Nevada National Security Site experimental P Tunnel. Test series here help develop researchers new ways to identify differentiate between seismic activity or whether an adversary is hiding low-yield nuclear testing or developing nuclear weapons in violation of treaties. These tests improve U.S. arms control and nuclear nonproliferation verification and monitoring capabilities. 

 

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.
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Screening Earthquakes from Explosions: Many of sources of energy that are underground shake the ground. Some of them are made by humans, like the explosions, and some of them occur naturally, like earthquakes that occur along faults. Explosions tend the shake the ground by the same amount if sensors or observers are the same distance from the explosion. Earthquakes shake the ground in a pattern that depends on the fault’s direction and shape. Scientists on the GEM team consider ground motion patterns that can result from explosions alone like that shown on the left, or even ground motion that results from a combination of both explosions and fault motion that create distinct patterns of ground motion. These patterns of ground motion allow scientists to infer if the source was only an explosion, or if it could also include fault motion.
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This collaborative model image shows a globe map that is falsely colored to indicate where Earth materials absorb the energy of seismic waves, and therefore attenuate seismograms and makes them more difficult to detect or analyze. Researchers call this a tomographic image, and call the measure Earth's tendency to absorb seismic energy "Q", which is the inverse of attenuation. The blue colors show areas where attenuation is small and signal amplitudes are not as absorbed as in red regions, where attenuation is high. An explosion or earthquake in a blue region is therefore more likely to be detected as an explosion or earthquake of the same size that is emplaced in a red 

 

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Map of seismicity from the 2022 Tonga volcanic eruption (gold) and nearby stations (triangles), with lines showing the great-circle arc and their source-to-station distance. 

 

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Mining blasts produce signals that can look like nuclear explosions, and the GEM team uses mining explosions to better understand how seismic signals might be generated by small nuclear tests. One processing method uses something like polarization, which provides evidence that a seismic source produced signals called Rayleigh waves. Explosions produce stronger Rayleigh waves compared to deep earthquakes. This figure shows polarization analysis results from a mine blast that occurred in the Powder River Basin, Wyoming on 07/29/2010 07:32:10 UTC. The signals were recorded along an east-west transect of an array of seismic sensors (a) that cross a large basin (PRB), mountains (BHM), and another basin (BHB). (b) shows the original vertical displacement seismograms, (c) shows the signal's prograde polarized component (that means the signals from the explosion move atypically), and (d) shows the retrograde polarized part of the signal (that means the signals move as expected). The impact of these results shows that mining explosions can move the ground in unexpected ways, and such seismic analysis helps researchers understand how nuclear explosions might produce similar signals.