Large-scale 3-D modeling by integration of resistivity models and borehole data through inversion

Abstract

Abstract. We present an automatic method for parameterization of a 3-D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of further detailing of geological models, or as direct input into groundwater models. The parameter of interest is the clay fraction, expressed as the relative length of clay units in a depth interval. The clay fraction is obtained from lithological logs and the clay fraction from the resistivity is obtained by establishing a simple petrophysical relationship, a translator function, between resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3-D clay fraction model, which holds information from the resistivity data set and the borehole data set in one variable. Finally, we use kmeans clustering to generate a 3-D model of the subsurface structures. We apply the procedure to the Norsminde survey in Denmark, integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey in the parameterization of the 3-D model covering 156 km 2 . The final five-cluster 3-D model differentiates between clay materials and different high-resistivity materials from information held in the resistivity model and borehole observations, respectively.

Performance evaluation of groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs

“…Figure 3 shows a map of the area delineating the study area boundary, streams, and hydrological data. An overview of the geophysical and lithological data can be found in Foged et al (2014). Within 5-7 km from the sea, the land is flat and rises only to 5-10 m a.s.l.…”

“…The borehole descriptions are from the Danish Jupiter database (GEUS, n.d.) and the level of detail and quality varies from detailed lithological description at 1 m intervals to more simple sand, clay, till descriptions at layer interfaces. A thorough description of EM data collection and processing and lithological borehole information can be found in Foged et al (2014).…”

“…The concept is described in detail in Foged et al (2014) and Christiansen et al (2014), and only a brief introduction is given here. The inversion minimizes the difference between observed clay fraction as determined from borehole lithological logs (in the inversion this is the data) and translated clay fraction as determined from geophysical resistivity values (in the inversion this is the forward data).…”

Anthropogenic wetlands due to over-irrigation of desert areas: a challenging hydrogeological investigation with extensive geophysical input from TEM and MRS measurements

“…The TEM method measures the electrical resistivity of the subsurface, which can be linked to water quality and soil type, in a threedimensional (3-D) setting (Foged et al, 2014). The MRS method is used to measure free water content (i.e., the total volume of water that can freely move and is not bound to the grain surface) directly and provides information on pore size distribution (Legchenko et al, 2004).…”

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

“…This is particularly true for data collected by airborne electromagnetic methods (AEM) because they can be collected quickly, densely, and at a relatively low cost for the very large spatial coverage (Steuer et al, 2008;Viezzoli et al, 2010b;Abraham et al, 2012;Faneca Sànchez et al, 2012;Refsgaard et al, 2014;Munday et al, 2015). Large-scale AEM (or ground-based EM) investigations have been used to delineate aquifers, aquitards, and buried valleys or other structures containing aquifers Jørgensen et al, 2003;Abraham et al, 2012;Oldenborger et al, 2013), to assess aquifer vulnerability Foged et al, 2014), to map saltwater intrusion (Fitterman and Deszcz-Pan, 1998;Viezzoli et al, 2010b;Lawrie et al, 2012;Herckenrath et al, 2013b), and to map freshwater resources (Steuer et al, 2008;Faneca Sànchez et al, 2012;Munday et al, 2015). The main drawbacks of electromagnetic (EM) data are (1) ambiguity in relating electrical properties to hydraulic properties, and (2) reduced lateral and vertical resolution with depth.…”