Tham khảo tài liệu 'autonomous underwater vehicles part 14', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Mapping and Dilution Estimation of Wastewater Discharges Based on Geostatistics Using an Autonomous Underwater Vehicle 249 Temperature@ m Temperature@ m Samples 20 026 10 506 Mean C C Median C C Minimum C C Maximum C C Coefficient of skewness Coefficient of variation Table 1. Summary statistics of temperature measurements. Salinity@ m Salinity@ m Samples 20 026 10 506 Mean psu psu Median psu psu Minimum psu psu Maximum psu psu Coefficient of skewness Coefficient of variation Table 2. Summary statistics of salinity measurements. Temperature C Temperature C Fig. 4. Histograms of temperature measurements at depths of m left and 3 m right . 250 Autonomous Underwater Vehicles Salinity psu Salinity psu Fig. 5. Histograms of salinity measurements at depths of m left and 3 m right . Variogram modeling For the purpose of this analysis the temperature and the salinity measurements were divided into a modeling set comprising 90 of the samples and a validation set comprising 10 of the samples . Modeling and validation sets were then compared using Student s-t test to check that they provided unbiased sub-sets of the original data. Furthermore sample variograms for the modeling sets were constructed using the MME estimator and the CRE estimator. This robust estimator was chosen to deal with outliers and enhance the variogram s spatial continuity. An estimation of semivariance was carried out using a lag distance of 2 m. Table 3 and Table 4 show the parameters of the fitted models to the omnidirectional sample variograms constructed using MME and CRE estimators. All the variograms were fitted to Matern models for several shape parameters v with the exception to the salinity data measured at the depth of 3 m. The range value in meters is an indicator of .
