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    Enhancing national security through the research, development and utilization of spatial sciences.

    The SSENS Team specializes in integrating geologic, geomorphic and remote sensing observations to achieve national security objectives, while also utilizing the same data to understand seismic and climate-related hazards.

    The Spatial Sciences to Enhance National Security Team (SSENS) Team members are experts in geologic characterization, seismic hazards, field and structural geology, petroleum geology, remote sensing, 3D subsurface geologic modeling, network seismology, environmental compliance and database management. They are also skilled in integrating observations obtained through fieldwork and remote sensing techniques.

    What We Do

    • Integration of geologic, geomorphic and structural geologic observations with novel near-surface remote sensing tools to discriminate and analyze surface changes to achieve national and global security objectives.
    • Develop new algorithms and coupling strategies for monitoring signals using optical fiber distributed acoustic sensing (DAS).
    • Operate, maintain and analyze data from the Los Alamos Seismic Network (LASN) in support of LANL’s Seismic Hazards Program.
    • Laboratory-scale acoustics experiments on porous rocks in support of oil and gas research.
    • Utilize seismic observations to locate and model the source properties of earthquakes.
    • Implementation of research focused on better understanding the regional geologic setting to better constrain uncertainties in hazards calculations.
    • Provide geologic characterization and consult on the potential seismic geology hazards to new and existing LANL facilities.
    • 3D Subsurface geoscience, geophysics and geology, which includes 3D geologic framework modeling and subsurface interpretation, seismology and seismic data interpretation and surface geology.
    • Production of local-to-global cartographic assignments and geospatial analyses in GIS and ESRI online applications.
    • Full 3D waveform measurements using laser vibrometry.
    • Combining field-based and remotely sensed observations to assess faults, volcanoes and ice sheets to better understand their geologic and climate hazards.
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    Accoustics Lab
    Johnson Learning Machine
    Ryan McGuill with Wire Line Tool
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    Ryan McGuill with Wire Line Tool

    Primary Expertise

    • Field geology and structural geology.
    • Geospatial analysis and remote sensing.
    • Structure-from-Motion (SfM) photogrammetry.
    • Geologic framework modeling and subsurface characterization.
    • Uncrewed Aerial System (UAS) operations, sensor deployment and data analysis/fusion.
    • Nuclear nonproliferation.
    • Detection, characterization and discrimination of geologic signatures from underground explosions.
    • Comprehensive Nuclear Test Ban Treaty technical subject matter expertise support and training.
    • Earthquake triggering and earthquake processes.
    • Ground-based nuclear detonation detection.
    • Improved technologies to support sustainable energy (renewable, nuclear and fossil fuel).
    • Large-scale plate tectonic processes.
    • Seismic and medical imaging.
    • Nondestructive evaluation.
    • Numerical modeling of geodynamic processes.

    Featured Research

    • Orphan Wells (UOWP): collaboration across five national labs to identify and characterize unidentified oil and gas wells utilizing airborne (UAV) sensors and ground-based field mapping.
    • Los Alamos Seismic Network (LASN) seismicity and ground motion monitoring uses seismic data recorded by LASN to characterize local earthquake activity and site response in support of updating LANL’s DOE-required Probabilistic Seismic Hazards Analysis (PSHA).
    • Probabilistic Seismic Hazards Analysis (PSHA) is a study that evaluates and updates the seismic safety bases of existing nuclear facilities at the Laboratory. 
    • PTS21 project to achieve high-fidelity vibrational information with an ultra-thin, self-powered sensor to ultimately develop next-generation, vibrational sensors for use across multiple Department of Energy/National Nuclear Security Administration missions.
    • FLONOISE – is a project that utilizes fiber optics and machine learning to characterize fluid transport.
    • LDRD ML – we are crafting a methodology to identify metal fragments post explosion using thermal imagery and machine learning image classification.
    • GDSA - this project is an ongoing effort to model long term storage of nuclear waste in alluvial basins. The focus of our team is creating realistic 3D earth models for simulation of fluid and gas transport.