PVGeo: an open-source Python package for geoscientific visualization in VTK and ParaView

Sullivan, Bane; Trainor‐Guitton, Whitney

doi:10.21105/joss.01451

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…The first part (a) of step (2) is the boundary edge detection of each element of the as-design model. By using pyvista's extract feature edges (Sullivan and Trainor-Guitton, 2019), the edges of the as-built model can be found. These are located in a georeferenced coordinate system, such as Lambert 72 in Belgium.…”

Section: Methodsmentioning

confidence: 99%

Phased Accuracy Analysis in Road Construction: Using Bim and Photogrammetric Output

De Winter,

Bassier,

De Geyter

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The construction industry is experiencing a gradual shift towards digitization. Currently, this digitization is relatively limited, but the need for that digitization as well as for automation is large. Digitization in road construction in particular is even more dire than in the construction industry in general. It involves various tasks such as progress, quality and quantity analyses. In this paper a proof of concept is presented on the possible automation of accuracy analysis in road construction throughout the construction of a road. The used data is 3D as-design data on the one hand and multiple 3D as-built data on the other hand, with the former being modelled using the regulations formulated by the Flemish ”Departement van Mobiliteit en Openbare Werken” (MOW), and the latter being captured using a Real Time Kinematic Unmanned Aerial Vehicle (RTK-UAV). The feasibility of the proposed workflow is examined using a real test case. Conducting these quality checks regularly will provide a clear overview of the construction site progress. In this way, any delays or errors can be detected quickly, resulting in higher quality and reduced failure costs.

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Section: Methodsmentioning

confidence: 99%

Phased Accuracy Analysis in Road Construction: Using Bim and Photogrammetric Output

De Winter,

Bassier,

De Geyter

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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A 4D flow cardiovascular magnetic resonance study of flow asymmetry and haemodynamic quantity correlations in the pulmonary artery

et al. 2021

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Objective. In this paper we elucidate the asymmetric flow pattern and the haemodynamic quantity distributions and correlations in the pulmonary artery (PA) vasculature in healthy adults having structurally normal hearts, to provide reference on the flow characteristics in the PA and the right ventricle. Approach. Velocity data are acquired non-invasively from 18 healthy volunteers by 4D flow magnetic resonance imaging, resolved to 20 phases with spatial resolution 3 × 3 × 3 mm3. Interpolation is applied to improve the accuracy in quantifying haemodynamic quantities including kinetic energy, rotational energy, helicity and energy dissipation rate. These quantities are volumetrically normalised to remove size dependency, representing densities or local intensity. Main results. Flow asymmetry in the PA is quantified in terms of all the flow dynamic quantities and their correlations. The right PA has larger diameter and higher peak stroke velocity than the left PA. It also has the highest rotational energy intensity. Counter-rotating helical streams in the main PA appear to be associated with the unidirectional helical flow noticed in the left and the right PA near the peak systole. Significance. This study provides a fundamental basis of normal flow in the PA. It implies the validity to use these flow pattern-related quantitative measures to aid with the identification of abnormal PA flow non-invasively, specifically for detecting abnormalities in the pulmonary circulation and response to therapy, where haemodynamic flow is commonly characterised by increased vortical and helical formations.

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Temperature uncertainty modelling with proxy structural data as geostatistical constraints for well siting: an example applied to Granite Springs Valley, NV, USA

Trainor-Guitton,

Siler,

Ayling

2023

Geoenergy

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Utilizing existing temperature and structural geology information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aims of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations. The data used to support the modelling are measured temperatures and structural proxies from 3D geologic modelling (distance to fault, distance to fault intersections and terminations, Coulomb stress change and dilation tendency), the latter considered ‘secondary’ data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally-likely temperature models (i.e. realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: V prior and entropy. V prior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because of the dipping ellipsoid shape created and that the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e. linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g. dilation v. distance to fault termination) should be used given the specific geothermal system. In general, V prior from local varying mean results identify locations that are close to high values for the structural proxies: areas with higher probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comparison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

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PVGeo: an open-source Python package for geoscientific visualization in VTK and ParaView

Cited by 5 publications

References 4 publications

Phased Accuracy Analysis in Road Construction: Using Bim and Photogrammetric Output

Phased Accuracy Analysis in Road Construction: Using Bim and Photogrammetric Output

A 4D flow cardiovascular magnetic resonance study of flow asymmetry and haemodynamic quantity correlations in the pulmonary artery

Temperature uncertainty modelling with proxy structural data as geostatistical constraints for well siting: an example applied to Granite Springs Valley, NV, USA

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