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The GBLUP model yielded higher prediction accuracies than the other models for line B1 (Table 2).
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The ITU defined geometrical channel model is applied, where UE to macro links follow the urban macro model (UMa), which includes separate models for line-of-sight (LOS) and non-LOS (NLOS) [38].
The MTGBLUP model did not necessarily perform better than the other models for lines B1 and B2, but was able to yield substantial positive predictive correlations for line W1 when line B1, B2, or both were used for training.
In this paper we present an improved model for line and edge detection in cortical area V1.
Fig. 5 P-wave velocity model for line KPr4.
The density model for line OGr1 also incorporated the subducted plate at depths of ∼30 35 km.
Next, we tested modifications of the model for line SPr2, seeking one that explained the observed data from both seismic profiles.
To further fine-tune the velocity model, we applied tomographic inversion (Zhang and Toksöz, 1998) to the data from SPr2 using the final modification of the model for line SPr2 as an initial model.
The checkerboard test showed good recovery beneath the Bonin ridge for the final modification of the model for line SPr2, but the eastern edge of the model (520 570 km) was not resolved at depths greater than 10 km.
The travel-time fit for this model was as good as that of the original model, indicating that both the original model for line SPr2 and the final modification explain the observed data, but that the final modification provided an even more reasonable model because the modeled structures on both lines at the intersection point were identical.
Figure 3 Uncertainty model for lines.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com