Application of a semi-automatic method for sedimentological mapping

Sedimentological maps, which are useful for understanding sediment dynamics, modelling sediment transport, and supporting sediment management, are important components in analysing coastal environments. Usually, sedimentological maps are based on the collection of superficial samples with different spatial distributions; however, the density of sampling is often low due to the need for optimising time and cost. A low sampling density may not be an issue to map coasts with standard bathymetry and seaward-fining sediment distribution but become critical in the presence of bathymetric anomalies. In fact, both sedimentological and morphological settings are the result of the coastal processes, and in these cases, even advanced automatic interpolation techniques cannot produce maps that properly represent the sedimentological signature of the morphological set-up. The presence of relict landforms, the Isonzo River sediment supply, and the westward littoral drift make the littoral of Grado (North Adriatic Sea, Italy) a good example of such an anomalous coast. Here, the morphological and sedimentological settings, coupled with a sparse and irregular sampling distribution, make an experienced assessment necessary to identify and solve critical issues in the sedimentological maps obtained through automatic algorithms. To construct more reliable maps of the study area, we propose a semi-automatic method based on four steps: (1) identify the incongruities of the automatic models by a match with the landforms; (2) draw polylines between samples to manually force the direction of interpolation; (3) generate simulated samples on the polylines in order to (4) interpolate both collected and simulated samples. In the study area, this approach was able to effectively represent the sedimentary anomalies caused by relict and active morphologies, both interpolating current samples and re-analysing old data with different sampling distribution. Each grain-size parameter distribution can be modelled using this technique, which can also be applied in other fields. The main benefits of this method are its ability to (1) increase useful data density without spending too much time on sediment sampling, (2) re-analyse old data, and (3) tune models only where they are unreliable.