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In this study, observations from a river-rehabilitation experiment involving gravel augmentation and spawning habitat enhancement were used to assess sources and magnitudes of error in depth, velocity, and shear velocity predictions made at the 1-m scale with a commercial two-dimensional (depth-averaged) model.
The Kirchhoff method results in slightly more error in depth and also takes more computational time.
Such velocity errors would result in a maximum of approximately 5%to10%0% error in depth that would not affect our structural interpretations.
The Table 1 also includes the targets numbered as 4, 5 and 6 considered in Fig. 8b, showing the error in depth as −0.38 and −0.785 cm lateral error.
Maximum error in lateral position is of one coordinate corresponding to 2.3 cm and maximum error in depth position is of 3 coordinates corresponding to 2.34 cm in F-K migration.
The Table 2 is a result of F-K migration on multiple targets which shows that there is no error in lateral position and −2.34 cm is the maximum error in depth is for the targets numbered as 1, 2 and 3 considered in Fig. 9a.
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Given this complexity, we thought it important to explore pneumonia treatment errors in-depth.
Compared with the original one in ASW, ours can reduce the errors in depth discontinuities, repetitive patterns and low texture regions effectively and improve the matching accuracy significantly.
Mapping of this horizon assists in likely reduction of errors in depth conversions and its dependent parameters.
However, these techniques may introduce errors in depth as large as 6 mm for 95% of the cases [ 24].
However, event depth is the most difficult parameter to be resolved when there are no nearby stations, and the error in relative depth is about 5 km for the station distribution in this area.
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