3D adaptive tomography using Delaunay triangles and Voronoi polygons

Böhm, Gualtiero; Galuppo, P.; Vesnaver, Aldo

doi:10.1046/j.1365-2478.2000.00211.x

Cited by 70 publications

(34 citation statements)

References 19 publications

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“…Nelson 1979;Jasny 1988;Haydon and Pianka 1999;Wilkinson et al 1999;Bohm et al 2000;Duyckaerts and Godefroy 2000;Zhan and Troy 2000;Blower et al 2003;Dupuis et al 2004), but because this is Houghton et al (1996, unpublished data), showing the 'sectorisation' ( red lines) of the Ruapehu deposit used to calculate the total grain-size distribution (technique B). The mass per unit area (M/A) characteristic for each sector is also indicated.…”

Section: Grain-size-calculation Techniquesmentioning

confidence: 99%

Total grain-size distribution and volume of tephra-fall deposits

2005

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On 17 June 1996, Ruapehu volcano, New Zealand, produced a sustained andesitic sub-Plinian eruption, which generated a narrow tephra-fall deposit extending more than 200 km from the volcano. The extremely detailed data set from this eruption allowed methods for the determination of total grain-size distribution and volume of tephra-fall deposits to be critically investigated. Calculated total grain-size distributions of tephra-fall deposits depend strongly on the method used and on the availability of data across the entire dispersal area. The Voronoi Tessellation method was tested for the Ruapehu deposit and gave the best results when applied to a data set extending out to isomass values of <1 g m 2 .The total grain-size distribution of a deposit is also strongly influenced by the very proximal samples, and this can be shown by artificially constructing subsets from the Ruapehu database. Unless the available data set is large, all existing techniques for calculations of total grain-size distribution give only apparent distributions. The tephra-fall deposit from Ruapehu does not show a simple exponential thinning, but can be approximated well by at least three straight-line segments or by a power-law fit on semi-log plots of thickness vs. (area) 1/2 . Integrations of both fits give similar volumes of about 410 6 m 3 . Integration of at least three exponential segments and of a power-law fit with at least ten isopach contours available can be considered as a good estimate of the actual volume of tephra fall. Integrations of smaller data sets are more problematic.

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Section: Grain-size-calculation Techniquesmentioning

confidence: 99%

Total grain-size distribution and volume of tephra-fall deposits

2005

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“…Our resolution-based adaptive meshing algorithm builds on ideas presented by Böhm et al ͑2000͒ andAjo-Franklin et al ͑2006͒. An overview of the processing steps needed for attenuation-difference analysis is outlined in Figure 1.…”

Section: Resolution-based Adaptive Meshingmentioning

confidence: 99%

Ground-penetrating-radar reflection attenuation tomography with an adaptive mesh

Hinz

Bradford

2010

GEOPHYSICS

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Ground-penetrating radar ͑GPR͒ attenuation-difference analysis can be a useful tool for studying fluid transport in the subsurface. Surface-based reflection attenuation-difference tomography poses a number of challenges that are not faced by crosshole attenuation surveys. We create and analyze a synthetic attenuation-difference GPR data set to determine methods for processing amplitude changes and inverting for conductivity differences from reflection data sets. Instead of using a traditional grid-based inversion, we use a data-driven adaptive-meshing algorithm to alter the model space and to create a more even distribution of resolution. Adaptive meshing provides a method for improving the resolution of the model space while honoring the data limitations and improving the quality of the attenuation difference inversion. Comparing inversions on a conventional rectangular grid with the adaptive mesh, we find that the adaptively meshed model reduces the inversion computation time by an average of 75% with an improvement in the root mean square error of up to 15%. While the sign of the conductivity change is correctly reproduced by the inversion algorithm, the magnitude varies by as much as much as 50% from the true values. Our heterogeneous conductivity model indicates that the attenuation difference inversion algorithm effectively locates conductivity changes, and that surface-based reflection surveys can produce models as accurate as traditional crosshole surveys.

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“…Domain regularization methods partition the domain enclosed by reconstructed surfaces into a finite number of tetrahedrons. Each small finite volume is logically linked to its neighbors thus forming a connected computational mesh (Bohm et al, 2000;Watson, 1981). In the grid generation steps, optimization algorithms divide the domain to preserve suitable aspect ratios of the finite volumes of the computational domain (Gambit, Ansys Inc.).…”

Section: Patient-specific Image Reconstructionmentioning

confidence: 99%