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Quantitative Visualization in CoViz 4D

CoViz 4D is a powerful team visualization tool and the premier environment for integrated Quantitative Visualization. The software includes advanced tools for rapid, integrated, quantitative analysis and study of diverse 3D and 4D data sets. While typically used in high-risk environments such as oil and gas fields where multiple dynamic data streams lead to an evolving decision-making process, CoViz 4D naturally extends well beyond the energy industry to other multi-data, time critical environments such as geospatial analysis and military operational scenarios.

Quantitative Visualization in the Energy Industry

The quantitative analysis of 4D seismic and attribute data is achieved through interactive viewer functionality or command line / scripting, including:

  • Easy visual filtering to define the areas and data of interest
  • Rapid arithmetic operations to capture changes from a specified timestep or the delta between timesteps
  • Interactive tools for property sum and average extractions in seismic and geocellular space
  • Back-interpolations of property values between spatially and temporally overlapping data
  • Statistical Analysis and Cross-plotting of diverse data types with interactive links back into the 4D viewing space
  • Multiple Output options to capture and export the results of the quantitative analysis

Separately, each of these operations provides a valuable addition to the data fusion environment. When taken together, this sequence of operations offers a potentially very powerful workflow for the Quantitative Analysis of temporal data. Temporal seismic and simulation data can be easily compared for better history matching and a fuller understanding of the 4D seismic response—all within an integrated visual framework accessible to the entire asset team.

A seismic discontinuity attribute (“coherency cube”) from around a North Sea Salt dome (in color) has been back-interpolated into the well data. This would allow, for example, the direct comparison of fault/fracture information from the seismic attribute with similar information recorded in image ⁄ well logs. Data used with permission of owner.

Cross-plot showing data points colored by a third attribute, with the statistics linked back spatially and temporally to the 3D ⁄ 4D viewing space. When the 4D viewing space is animated in time, the cross-plot changes to reflect the current seismic and simulation time-points. When points or regions are selected in the cross-plot, they are highlighted in the 4D viewing space, and vice versa. Data used with permission of owner.

Quantitative Visualization in Geospatial Analyses / Military Applications

Once the 3D scene is built from diverse data streams, and routes and other movements are captured, numerous 3D and 4D quantitative analyses can be run for a range of operational scenarios. Examples include.

  • Delineation of low, medium and high- risk landing zones based on maximum slope requirements, horizontal and vertical obstruction clearances, etc.
  • Clearance calculations such as distance from a flightpath to buildings and other vertical hazards
  • Asset visibility, line of sight and line of fire based on terrain, vegetation and man-made structures
  • Delineation of threat envelopes
  • Bearing and range distance to targets and/or landing zones from route position(s)
  • Route distance for fuel calculations and estimated travel times
  • Slope and vegetation analysis to indicate both suitable and hazardous vehicle travel areas

Based on interpreted results of various analyses described above, routes and other movements can be evaluated and adjusted to understand the best possible mission scenarios going forward. Vehicles, aircraft, water-based and human asset movement can be animated along their proposed routes visualizing the temporal sequence and relationship to the geospatial data. As routes are traveled, if a new threat envelope appears on the horizon, the mission route can be automatically changed to alert the user of the new threat level (e.g., flight paths change from low-risk green to high-risk red) and the CoViz 4D spatial engine can quickly calculate new routes to circumvent proximities to the threats.

Ground mission scene showing Humvee location and surrounding terrain, buildings and vegetation. Slope analysis of the terrain can be carried out to determine safe vehicle travel areas and potential hazards.

Airborne mission showing surface DEM, flightpath and buildings. Building coloration reflects clearance distance from aircraft, and coloring changes on-the-fly as a mission route is animated and rehearsed.

The utility of CoViz 4D in the above activities is flexible and adaptable to many other operational scenarios and mission critical applications. The power of dynamic Visual and Quantitative Analytics focuses on easier access and integration of complex 3D and 4D datasets so that more time can be spent on data analysis and risk reduction.

Data Sources/Credits:

“Grigsby, Shane, 2013, Leaf-on LiDAR point cloud data for solar site assessment of the CU-Boulder campus, Department of Geography, University of Colorado at Boulder, digital media.”

DEM data downloaded via Open Topography API:

Data obtained from OpenTopography are free of all copyright restrictions and made fully and freely available for both non-commercial and commercial uses.
DEM are from the SRTM (Shuttle Radar Topography Mission); The Shuttle Radar Topography Mission (SRTM) obtained elevation data on a near-global scale to generate the most complete high-resolution digital topographic database of Earth. SRTM consisted of a specially modified radar system that flew onboard the Space Shuttle Endeavour during an 11-day mission in February of 2000. SRTM is an international project spearheaded by the National Geospatial-Intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA).

Building Shape data downloaded via AWS OpenCity Model Data s3 bucket:

Open City Model is an initiative to provide cityGML data for all the buildings in the United States. By using other open datasets in conjunction with our own code and algorithms it is our goal to provide 3D geometries for every US building.


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