TRANSMISSIVITY AND MORPHOLOGICAL FEATURES IN A CHALK AQUIFER - A GEOSTATISTICAL APPROACH OF THEIR RELATIONSHIP

Authors
Citation
P. Bracq et F. Delay, TRANSMISSIVITY AND MORPHOLOGICAL FEATURES IN A CHALK AQUIFER - A GEOSTATISTICAL APPROACH OF THEIR RELATIONSHIP, Journal of hydrology, 191(1-4), 1997, pp. 139-160
Citations number
31
Categorie Soggetti
Engineering, Civil","Water Resources","Geosciences, Interdisciplinary
Journal title
ISSN journal
00221694
Volume
191
Issue
1-4
Year of publication
1997
Pages
139 - 160
Database
ISI
SICI code
0022-1694(1997)191:1-4<139:TAMFIA>2.0.ZU;2-8
Abstract
Whether for the management of an aquifer or to locate new wells, hydro geologists have always tried to establish transmissivity maps of regio nal aquifers with the highest possible accuracy. Classically, the tran smissivity is obtained from well tests and/or from calibrating a groun dwater flow model on the piezometric data, In this paper, we investiga te the possibility of adding new information to improve the estimates of the transmissivity in a shallow chalk aquifer, i.e. the characteris tics of certain morphological features, here referred to as 'lynchets' , which are short lineaments that can be seen on aerial photographs or on topographic maps and represent a sudden breach in slope of the gro und surface. We implicitly associate the density of these features wit h the density of vertical fracturing of the chalk, which is linked to the transmissivity, We selected the chalk aquifer of Northern France t o test this approach. Using numerical and geostatistical techniques to construct maps of the density of the lynchets, we compared them with transmissivity maps produced either by calibrating a groundwater flow model with piezometric data or by kriging the transmissivity values ob tained with well tests, We conclude that the lynchet information is cl early correlated with the transmissivity, and that this information ca n best be used by cokriging as a second variable associated with the w ell test data. Incidentally, to fit our geostatistical models of spati al variability (variogram), we use a new technique called the 'integra l semi-variogram', which is ideally suited to cases such as this where the spatial distribution of the data is such that the data points can not easily be grouped into pairs of increasing lag distance. This new technique is described briefly. (C) 1997 Elsevier Science B.V.