The DOE Knowledge Base Mthodology for the Creation of an Optimal Spatial Tessellation
The DOE Knowledge Base is a library of detailed information whose purpose is to improve the capability of the United States National Data Center (USNDC) to monitor compliance with the Comprehensive Test Ban Treaty (CTBT). Much of the data contained by the Knowledge Base is spatial in nature, and some of it is used to improve the accuracy with which seismic locations are determined while maintaining or improving current calculational perfor- mance. In this presentation, we define and describe the methodology used to create spatial tessellations of seismic data which are utilized with a gradient-modified natural-neighbor interpolation method to evaluate travel-time corrections. The goal is to interpolate a specified correction surface, or a group of them, with prescribed accuracy and surface smoothness requirements, while minimizing the number of data points necessary to represent the surface. Maintain- ing accuracy is crucial toward improving the precision of seismic origin location. Minimizing the number of nodes in the tessellation improves calculational and data access efficiency and performance. The process requires two initialization steps and an iterated 7 step algorithm for inserting new tessellation nodes. First, M residual data from ground truth events are included in the tessellation. These data remain fixed throughout the creation of the triangular tessellation. Next, a coarse grid of nodes is laid over the region to be tessellated. The coarse grid is necessary to define the boundary of the region to be tessellated. Next the 7 step iterated algorithm is performed to add new nodes to the tessellation to ensure that accuracy and smoothness requirements are met. These steps include 1) all data points in the tessellation are linked together to form a triangular tessellation using p standard Delaunay tessellation technique; 2) all of the data points, excluding the original data and boundruy nodes, are smoothed using a length-weighted Laplacian smoother to remove poorly formed triangles; 3) all new data points are assigned corrections by performing a Non-stationary Bayesian Kriging calculation for each new triangle node; 4) all nodes that exceed surface roughness requirements are split by inserting a new node at the mid-points of the edges that share the rough nod% 5) all remaining triangle edge midpoints and centers are inte~olated using gradient-modified natural-neighbor interpolation and kriged using the Bayesian IGiging algoritlm 6) new nodes are inserted into the tessellation at all edge and triangle mid-points that exceed the specified relative error tolerance between the interpo- lated and Iaiged values, and 7) all new insertion nodes are added to the tessellations node list. Steps 1 through 7 are repeated until all relative error and surface smoothness requirements are satisfied. Results indicate that node densities in the tessellation are largest in regions of high surface curvature as expected. Generally, gradient modified natural-neighbor interpolation methods do a better job than linear natural-neighbor methods at meeting accuracy requirements which translates to fewer nodes necessary to represent the surface.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1057
- Report Number(s):
- SAND98-2348C; ON: DE00001057
- Resource Relation:
- Conference: 20th Annual Seismic Research Symposium for Monitoring A Comprehensive Test Ban Treaty: Santa Fe, NM; 09/21-23/1998
- Country of Publication:
- United States
- Language:
- English
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