title: Comparison of geostatistical kriging algorithms for intertidal surface sediment facies mapping with grain size data.
abstract: This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then mapped. As grain size fractions are compositional data, their characteristics should be considered during spatial prediction. For efficient prediction of compositional data, additive log-ratio transformation is applied before cokriging analysis. The predictive performance of cokriging of the transformed variables is compared with that of cokriging of raw fractions in terms of both prediction errors of fractions and facies mapping accuracy. From a case study of the Baramarae tidal flat, Korea, the mapping method based on cokriging of log-ratio transformation of fractions outperformed the one based on cokriging of untransformed fractions in the prediction of fractions and produced the best facies mapping accuracy. Indicator kriging that could not account for the variation of fractions within each facies type showed the worst mapping accuracy. These case study results indicate that the proper processing of grain size fractions as compositional data is important for reliable facies mapping.
authors.affiliation: Department of Geoinformatic Engineering, Inha University, Incheon 402-751, Republic of Korea.
authors.forename: No-Wook
authors.identifier: 0000-0002-9778-3624
authors.initials: NW
authors.lastname: Park
authors.affiliation: Department of Geography, Kongju National University, Kongju 314-701, Republic of Korea.
authors.forename: Dong-Ho
authors.identifier: 0000-0003-1433-2009
authors.initials: DH
authors.lastname: Jang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004784:Environmental Monitoring / Q000379:methods*; D062305:Geographic Mapping; D019015:Geologic Sediments / Q000032:analysis* / Q000737:chemistry*; D015233:Models, Statistical; D010316:Particle Size; D062211:Spatio-Temporal Analysis; D055871:Tidal Waves / Q000706:statistics & numerical data*