We specialize in wave physics. Leaders in sound wave analysis, we perform advanced computational mathematics and experiments to reveal hidden behavior in complex systems. Our team of nonlinear acoustics and machine learning experts support national security and protect human health with a myriad of innovations in fossil energy acquisition, greenhouse gas capture and storage, earthquake prediction, and cancer diagnostics.
- Geothermal energy and subsurface characterization.
- Creating advanced algorithms for seismic and medical imaging.
- Applying machine learning expertise to geophysics challenges.
- Performing experiments and modeling to reveal nonlinear elastic behavior in geomaterials.
- Porous fluid flow and fracture analysis.
- Seismic network instrumentation and earthquake detection.
- Engineered time reversal for material characterization and nondestructive testing.
Nonlinear, Nonequilibrium Elasticity in Diverse Materials: applying diagnostics to understand dynamic nonlinear elastic behavior and nonequilibrium dynamics in diverse materials. Applications include:
- Borehole imagining for carbon sequestration and oil and gas.
- Crack and damage detection in industrial parts.
- Diagnostic development for the Laboratory’s national security mission needs.
- Analyzing earthquake seismology dynamics (e.g., strong ground motion).
Time Reversal (TR): TR is recording and reversing waves to place acoustic or seismic energy precisely in time and space. TR has applications in earthquake source analysis, and is studied in two practical and fundamental ways:
- The focusing abilities of TR can be combined with Nonlinear Elastic Wave Spectroscopy to locate and image nonlinear scatter sources on or near the surface.
- A source inside a solid can be located using recorded data on the surface by time reversing the data and back-propagating it through a velocity model.
Seismic Network: managing and monitoring the Los Alamos Seismic Network (LASN) for earthquake location services and research. LASN has operated since 1973, and been used to report information on more than 2,500 earthquakes. We currently operate nine earthquake monitoring stations with various sensors and instruments. The network can also be used as a seismic laboratory to look at other signals of interest.
Porous Fluid Flow: performing laboratory and field tests to quantify the conditions and physical mechanisms under which seismic stimulation can increase oil recovery. We conduct laboratory experiments with a range of different formation rock types and composite samples utilizing the unique capabilities of the Los Alamos Dynamic Stress Stimulation Laboratory. Objectives include:
- Determining the optimum wave-field parameters for effective treatment over a wide range of field conditions.
- Obtaining a fundamental scientific understanding of the relative importance of the physical mechanisms governing the stimulation phenomenon.
Machine Learning: Our team has expertise in using machine learning to detect important signals that appear to be noise and would otherwise be missed by traditional means. We have successfully demonstrated the ability of machine learning to predict the timing of earthquakes generated in the laboratory. Research is also underway to apply machine learning to earth scale signals, and to identify signatures of other systems, such as carbon dioxide leakage in carbon sequestration reservoirs.
Seismic and Acoustic Imaging: conducting basic and applied research in wave propagation, seismic imaging, scattering, and the interaction of acoustic waves with rock mass structure, fabric, and pore fluids, and medical imaging. We are developing and testing a wide range of new methods for rapid modeling of seismic wave propagation and for obtaining improved seismic images of the Earth’s subsurface. 3-D imaging and modeling projects conducted in collaboration with the Department of Energy and the petroleum industry include:
- Developing new methods, implementing them on parallel computers, and investigating the range of applicability of the methods by doing tests on synthetic (numerical) and field datasets.
- Investigating improvements to the standard ray-theoretical Kirchhoff-based approach.
- Evaluating elastic and anisotropic wave propagation effects.
Medical Imaging: higher frequency medical acoustic imaging to improve breast and prostate and prostate cancer diagnosis.