980 resultados para 3D scalar data


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The seismic data were acquired north of the Knipovich Ridge on the western Svalbard margin during cruise MSM21/4. They were recorded using a Geometrics GeoEel streamer of either 120 channels (profiles p100-p208) or 88 channels (profiles p300-p805) with a group spacing of 1.56 m and a sampling rate of 2 kHz. A GI-Gun (2×1.7 l) with a main frequency of ~150 Hz was used as a source and operated at a shot interval of 6-8 s. Processing of profiles p100-p208 and p600-p805: Positions for each channel were calculated by backtracking along the profiles from the GI-Gun GPS positions. The shot gathers were analyzed for abnormal amplitudes below the seafloor reflection by comparing neighboring traces in different frequency bands within sliding time windows. To suppress surface-generated water noise, a tau-p filter was applied in the shot gather domain. Common mid-point (CMP) profiles were then generated through crooked-line binning with a CMP spacing of 1.5625 m. A zero-phase band-pass filter with corner frequencies of 60 Hz and 360 Hz was applied to the data. Based on regional velocity information from MCS data [Sarkar, 2012], an interpolated and extrapolated 3D interval velocity model was created below the digitized seafloor reflection of the high-resolution streamer data. This velocity model was used to apply a CMP stack and an amplitude-preserving Kirchhoff post-stack time migration. Processing of profiles p400-p500: Data were sampled at 0.5 ms and sorted into common midpoint (CMP) domain with a bin spacing of 5 m. Normal move out correction was carried out with a velocity of 1500 m s-1 and an Ormsby bandpass filter with corner frequencies at 40, 80, 600 and 1000 Hz was applied. The data were time migrated using the water velocity.

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The classification of airborne lidar data is a relevant task in different disciplines. The information about the geometry and the full waveform can be used in order to classify the 3D point cloud. In Wadden Sea areas the classification of lidar data is of main interest for the scientific monitoring of coastal morphology and habitats, but it becomes a challenging task due to flat areas with hardly any discriminative objects. For the classification we combine a Conditional Random Fields framework with a Random Forests approach. By classifying in this way, we benefit from the consideration of context on the one hand and from the opportunity to utilise a high number of classification features on the other hand. We investigate the relevance of different features for the lidar points in coastal areas as well as for the interaction of neighbouring points.

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To reconstruct Export Productivity (Pexp), 27 taxonomic categories of the planktonic foraminifera census data were used with the modern analog technique SIMMAX 28 (Pflaumann et al., 1996, doi:10.1029/95PA01743; 2003, doi:10.1029/2002PA000774). To the 26 taxonomic groups widely used and listed in Kucera et al. (2005, doi:10.1016/j.quascirev.2004.07.014), Turborotalita humilis was added in our calibration as it is associated with the PCC source region (Meggers et al., 2002, doi:10.1016/S0967-0645(02)00103-0). The modern analog file is based on the Iberian margin database (Salgueiro et al., 2008, doi:10.1016/j.marmicro.2007.09.003) combined with the North Atlantic surface samples used by the MARGO project (Kucera et al., 2005). This results in a total of 999 analogs for Pexp. Modern oceanic primary productivity (PP) is obtained for each site by averaging 12 monthly primary productivity values for a 8-year period (1978-1986) that were estimated from satellite color data (CZCS) and gridded at 0.5° latitude - longitude fields (Antoine et al., 1996, doi:10.1029/95GB02832). Export Productivity (Pexp) was calculated from the PP values following the empirical relationship Pexp = PP**2/400 for primary production below 200 gC/m**2/yr, and Pexp = PP/2 for primary production above 200 gC/m2/yr (Eppley and Peterson, 1979, doi:10.1038/282677a0; Sarnthein et al., 1988, doi:10.1029/PA003i003p00361). The residuals gives the differences between satellite based Pexp and foraminiferal Pexp.