Overcoming inter-dataset discrepancies in the grain size distributions of fine-grained sediments by partial least squares regression: A case study of the Belgian Boom Formation

Abstract

The grain size distribution is an important property of all clastic sediments: it determines their mechanical properties and is directly related to their mode of transport and origin. Therefore, the accurate measurement and comparability of grain size data are important. The former has been studied in detail in literature and has been demonstrated to be significantly instrument-dependent, while the latter has not been given the same attention. The current study examined in detail a large set of grain size data measured on a single clay formation, the Oligocene Boom Formation, from which the large influence of sample preparation on the grain size distribution can be inferred. Especially the use of sonication to disintegrate silt-sized aggregates was found to be of a particularly big influence on the measured distributions. As a way to still be able to valorize non-comparable datasets, a statistical conversion procedure was introduced based on partial least squares regression in a compositional data space. The converted distributions follow the stratigraphical trends in grain size expected in the Boom Formation, while also being well correlated to hydraulic conductivity measurements performed on Boom Clay samples of similar depths. This is a strong indication that the conversion was successful. In the future, this approach can be used as a tool to harmonize any combination of compositional datasets, not just limited to grain size data, allowing a valorization of the maximal amount of data.