Spatial prediction of the variability of Early Pleistocene subsurface sediments in the Netherlands - Part 2: Geochemistry
Abstract We started a geochemical mapping campaign in the Early Pleistocene fluviatile Kedichem Formation in the Netherlands in order to meet the demand for more information about subsurface sediment compositions. Geochemical data were collected during a sampling campaign, and about 600 samples from the Kedichem Formation were analyzed. By linking the geochemical data with lithological classifications from the TNO-NITG borehole database, we established a geochemical prediction model. Elements were divided into classes according to their geochemical behaviour in relation to lithological paramet... Mehr ...
Verfasser: | |
---|---|
Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2000 |
Reihe/Periodikum: | Netherlands Journal of Geosciences - Geologie en Mijnbouw ; volume 79, issue 4, page 381-390 ; ISSN 0016-7746 1573-9708 |
Verlag/Hrsg.: |
Cambridge University Press (CUP)
|
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-27623680 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | http://dx.doi.org/10.1017/s0016774600021892 |
Abstract We started a geochemical mapping campaign in the Early Pleistocene fluviatile Kedichem Formation in the Netherlands in order to meet the demand for more information about subsurface sediment compositions. Geochemical data were collected during a sampling campaign, and about 600 samples from the Kedichem Formation were analyzed. By linking the geochemical data with lithological classifications from the TNO-NITG borehole database, we established a geochemical prediction model. Elements were divided into classes according to their geochemical behaviour in relation to lithological parameters. For each of the classes, we combined lithological groups in to groups with relevant geochemical differences. By calculating for each element the average composition in each of these groups, we were able to predict the geochemical composition of subsurface sediments by ‘translating’ the spatial lithological data from the TNO-NITG borehole database into geochemical data. We visualized this model by calculating and interpolating the average composition of horizontal slices of the Kedichem Formation. The model performance is fairly good, although it has a tendency to underestimate extreme values.