Comparing overlapping data Specify additional coverage and between platforms associated survey parameters to resolve Assessing coverage in shortcomings in survey data. relation with contour and features H6.2b Filtering Data cleaning techniques Identify and remove outliers and validate and estimation (manual and automated) data cleaning and other decisions made in of multi-beam Identification of outliers processing multi-beam data. data Identification and classification of systematic Interpret and resolve systematic errors errors detected during data processing (A) Total propagated uncertainty - horizontal Perform time series analysis of data from Total propagated uncertainty - multiple sensors to detect artefacts and vertical errors that may exist in a survey Comparing crossing and dataset. adjacent data between survey lines Assess data for coverage and Comparing overlapping data quality, and specify remedial surveys. between platforms H6.2c Spatial A posteriori and a priori Differentiate between relative and data quality propagated uncertainty absolute uncertainties. control (horizontal and vertical) Primary and secondary survey Estimate and compare uncertainties sensors used for quality control through the use of different spatial and (A) Relative and absolute temporal datasets. uncertainties Define procedures used to assess and accept or reject data. H6.2d Spatial 1D polynomial interpolation Choose an appropriate interpolation data Interpolating splines, method and compute a surface from interpolation multi-dimensional sparse survey measurements. (I) splines
Comparing overlapping data between platforms Assessing coverage in relation with contour and features Specify additional coverage and associated survey parameters to resolve shortcomings in survey data. H6.2b Filtering and estimation of multi-beam data (A) Data cleaning techniques (manual and automated) Identification of outliers Identification and classification of systematic errors Total propagated uncertainty - horizontal Total propagated uncertainty - vertical Comparing crossing and adjacent data between survey lines Comparing overlapping data between platforms Identify and remove outliers and validate data cleaning and other decisions made in processing multi-beam data. Interpret and resolve systematic errors detected during data processing Perform time series analysis of data from multiple sensors to detect artefacts and errors that may exist in a survey dataset. Assess data for coverage and quality, and specify remedial surveys. H6.2c Spatial data quality control (A) A posteriori and a priori propagated uncertainty (horizontal and vertical) Primary and secondary survey sensors used for quality control Relative and absolute uncertainties Differentiate between relative and absolute uncertainties. Estimate and compare uncertainties through the use of different spatial and temporal datasets. Define procedures used to assess and accept or reject data. H6.2d Spatial data interpolation 1D polynomial interpolation Interpolating splines, multi-dimensional splines Choose an appropriate interpolation method and compute a surface from sparse survey measurements. (I)