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In this study, we employ 2D image data (thin sections) for measuring critical microstructural features of commercial wicks for use in correlation-based prediction of transport properties.
To check the results, other data, such as mud loss data, thin section and VDL log, were used to support our interpretation.
The low-resolution data (thin curves) corresponds to Figure 18 and to a grid spacing of (0.2~M_{odot}).
Fig. 4 Selected field data (thin line) representing the observed data at the center of each block in the thin sheet and the calculated response of the inverse model (thick arrows), both for the period of 300 s.
Fig. 1 A typical geomagnetic storm onset as a CME shock front seen in the solar wind speed data (thin gray line, measured by ACE at a distance of 1.5 × 106 km) and an SSC on Earth (thick blue line, measured at the Conrad Observatory).
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Firstly, an initial data thinning is performed, to reduce the copious data set size, employing 3D spatial filtering.
The effect of data thinning on R2 hardly varied between simulations where the parameters were held constant or allowed to change.
Lidar-derived canopy surfaces (digital surface models, DSMs) at the field plot locations showed little variation with the different levels of data thinning.
The accuracy of the DTMs generated after data thinning was evaluated using the same approach as with the full-density DTM.
We simulated demographic patterns using parameters for YRI, CEU, and CHBJPT as described in Grossman et al. (2010) with a data thinning procedure that improves fitting to the 1000 Genomes empirical data (Engelken et al. 2014).
Importantly, submontane areas consistently showed larger changes in DTM accuracy than montane areas after data thinning – consistent with longer ranging distances, larger lidar footprints, and more complex topography at lower elevations in the study site.
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CEO of Professional Science Editing for Scientists @ prosciediting.com