基于粒度组成数学变换方法的海底沉积物类型图编制OA

To address current limitations in sediment-type mapping and to achieve unbiased geostatistical inference of sediment composition,we developed and validated a standardized"Composition-Classification"mapping workflow based on grain-size composition data from about 3 200 surface sediment samples taken from the Yangtze River Estuary-East China Sea shelf.The sand-silt-clay percentage data were transformed first using the additive log-ratio(ALR)transformation,followed by empirical Bayesian kriging(EBK)interpolation in the transformed space.An unbiased Monte Carlo(MC)back-transformation was then applied to recover the continuous spatial distribution of each component.At last,Folk's classification scheme was used on a cell-by-cell basis to produce posterior mean-probability and maximum-probability sediment-type maps.The workflow simultaneously outputs several diagnostic measures of uncertainty,including maximum class probability,probability difference(ΔP)and normalized entropy(Hn).Results show that the method could inherently satisfy non-negativity and closure constraints,and yield sediment-type maps with smooth spatial transitions and highly consistent spatial patterns in the diagnostic indices.Meanwhile,it robustly delineated high-confidence areas while identified low-confidence zones that require further optimization.The proposed method also supported rapid remapping to different management units,providing a technical support for marine resource exploration and environmental management.